CHANGELOG.md

# CHANGELOG

## 3.6.0 (2025-11-07)

MLflow 3.6.0 includes several major features and improvements for AI Observability, Experiment UI, Agent Evaluation and Deployment.

- 🔗 **Full OpenTelemetry Support in OSS Server**: MLflow now offers comprehensive OpenTelemetry integration, allowing you to ingest OpenTelemetry traces into MLflow and use both SDK seamlessly together. (#18540, #18532, #18357, @B-Step62, @serena-ruan)
- 💬 **Session-level View in Trace UI**: New chat sessions tab provides a dedicated view for organizing and analyzing related traces at the session level, making it easier to track conversational workflows. (#18594, @daniellok-db)
- 🧭 **New experiment tab bar**: The experiment tab navigation bar has been moved from the top of the page to the left side. As MLflow continues to grow, this layout provides more room to add new tabs while keeping everything easy to find. (#18594, @daniellok-db)
- 🚀 **New Supported Frameworks in TypeScript Tracing SDK**: Auto-tracing support for **Vercel AI SDK**, **Gemini**, **Anthropic**, **Mastra** in TypeScript, expanding MLflow's observability capabilities across popular JavaScript/TypeScript frameworks. (#18402, @B-Step62)
- 💰 **Tracking Judge Cost and Traces**: Comprehensive tracking of LLM judge evaluation costs and traces, providing visibility into evaluation expenses and performance with automatic cost calculation and rendering. (#18481, #18484, @B-Step62)
- ⚙️ **Agent Server**: New agent server infrastructure for managing and deploying scoring agents with enhanced orchestration capabilities. (#18596, @bbqiu)

Breaking changes:

- Deprecate pmdarima, promptflow, diviner flavors (#18597, #18577, @copilot-swe-agent)
- Drop numbering suffix (`_1`, `_2`, ...) from span names (#18531, @serena-ruan)

Features:

- [Tracing] Add RLIKE operator support for trace search (#18591, @serena-ruan)
- [Tracing] Attributes translation for OpenTelemetry clients (#18532, @serena-ruan)
- [Tracing] Implement auto-tracing logic for Vercel AI SDK (#18402, @B-Step62)
- [Tracing] Anthropic Typescript SDK (#18189, @joelrobin18)
- [Tracing] Support search by span details for traces in OSS MLflow server (#17918, @serena-ruan)
- [Tracing] Minor clean up for the trace summary view (#18436, @B-Step62)
- [Tracing] Log assessments to DSPy eval traces (#18136, @B-Step62)
- [Tracking] Add support for using the same DB for tracking and auth (#18384, @BenWilson2)
- [Tracking] Make Pytorch lightning autologging support logging model signature (#18510, @WeichenXu123)
- [Tracking] Make `mlflow.pytorch` pyfunc loader supporting pytorch forecasting model (#18428, @WeichenXu123)
- [Tracking] Job backend: Support creating virtual python environment for job execution (#18111, @WeichenXu123)
- [Evaluation] Add `search_traces` tool for agentic judge (#18228, @dbrx-euirim)
- [Evaluation] Record and render LLM judge cost (#18481, @B-Step62)
- [Evaluation] Frontend adjustments for handle judge traces (#18485, @B-Step62)
- [Evaluation] Record judge traces (#18484, @B-Step62)
- [Evaluation] Add support for profile usage in Databricks Agents dataset API operat… (#18431, @BenWilson2)
- [Evaluation] Add mlflow traces eval CLI command (#18069, @alkispoly-db)
- [Evaluation] Add mlflow scorers register-llm-judge CLI command (#18330, @alkispoly-db)
- [Evaluation] Add description property to Scorer interface (#18383, @alkispoly-db)
- [Evaluation] Allow passing empty scorer list for manual result comparison (#18265, @B-Step62)
- [Scoring] Introduce Agent Server (#18596, @bbqiu)
- [UI] Add chat sessions tab (#18594, @daniellok-db)
- [UI] Child Parent Link (#17248, @joelrobin18)
- [Models] Use UBJSON format as default for XGBoost models (#18420, @harupy)
- [Models] Support Langchain 1.x (#18490, @BenWilson2)
- [Model Registry] Add deprecation warnings for filesystem backends (#18524, @harupy)
- [Model Registry] Allow for skipping pip installation while packing environment for model serving (#18448, @juntai-zheng)
- [Gateway] Add configuration option for long-running deployments client requests (#18363, @BenWilson2)
- [Gateway] Make Openai, Anthropic, Gemini provider supporting streamed function calling response (#18367, #18328, #18294, @WeichenXu123)
- [Gateway] Add traffic route to multiple endpoints (#18064, @WeichenXu123)
- [Docs] Add Sticky Header to CodeBlock in MLflow/DOCS Code Examples (#18508, @PavithraNelluri)
- [Build / Evaluation] Add CLI command to list registered scorers by experiment (#18255, @alkispoly-db)

Bug fixes:

- [Evaluation] Fix plugin incompatibility with circular import (#18599, @BenWilson2)
- [Tracing] Paginate `delete_traces` calls to Databricks MLflow server (#18563, @dbrx-euirim)
- [Artifacts] Fix handling of `pathlib.Path` in `validation.py` (#16660, @benglewis)
- [Tracking] Enhance SqlAlchemyStore to include model outputs in run search results (#18568, @TomeHirata)
- [Prompts] Fix typo in gepa version (#18423, @TomeHirata)
- [Tracking] Add validation checks for search runs (#18487, @BenWilson2)
- [Tracking] Fix: Update run to use the new run name when resuming an existing run (#18511, @WeichenXu123)
- [UI] Fix search filter for metrics/params with spaces in names (#18503, @serena-ruan)
- [Evaluation] Remove the ability to register or load custom scorers (#18493, @BenWilson2)
- [UI] Fix assessment editing UI resetting field values when selecting name (#18474, @serena-ruan)
- [Evaluation] Add specificity to the system prompt for metrics (#18460, @BenWilson2)
- [Evaluation] [Eval #2] Support evaluating traces and linking to run in OSS (#18415, @B-Step62)
- [Tracking] Disable autologging for pytorch forecasting model predict method (#18444, @WeichenXu123)
- [Evaluation / Tracing] Reuse traces in genai.evaluate when endpoint uses dual-write mode (#18403, @harupy)
- [UI] Remove X-Frame-Options for notebook trace renderer (#18446, @TomeHirata)
- [Evaluation] Adjust util for remote tracking server declaration (#18411, @BenWilson2)
- [Evaluation / UI] Fix evaluation runs table link to point to traces tab instead of overview (#18332, @ritoban23)
- [Models] fix-streaming (#18337, @BenWilson2)
- [Evaluation / Tracing / Tracking] Job backend: Fix job store sql engine race condition (#18233, @WeichenXu123)
- [Evaluation] Add atomicity to job_start API (#18226, @BenWilson2)
- [Evaluation / Tracking] Job backend: Eager launch huey consumer to prevent Huey race condition (#18220, @WeichenXu123)

Documentation updates:

- [Docs] Add basic doc for Otel support (#18623, @B-Step62)
- [Docs] clarify datasets package requirement (#18610, @BenWilson2)
- [Evaluation] Deprecate v2 eval (#18470, @B-Step62)
- [Docs / UI] Add Sticky Header to CodeBlock in MLflow/DOCS Code Examples (#18508, @PavithraNelluri)
- [Docs] [Doc; 1/N] Clean up getting started for classical ML/DL (#18379, @B-Step62)
- [Docs] AI-gateway-revamp: Update doc (#18397, @WeichenXu123)
- [Docs] Fix documentation: update deprecated pandas fillna usage in classic-ml tutorial (#17927, @Kalindu-C)

Small bug fixes and documentation updates:

#18735, #18429, #18530, #18416, #18401, #18400, #18465, #18453, #18414, #18421, @B-Step62; #18641, #18631, #18629, #18605, #18426, #18603, #18526, #18587, #18583, #18564, #18536, #18544, #18567, #18565, #18533, #18535, #18501, #18498, #18368, #18357, #18471, #18476, #18356, #18214, #17975, @serena-ruan; #18600, #18604, #18602, #18566, #18549, #18538, #18517, #15849, #18492, #18468, #18475, #18469, #18467, #18452, #18449, #18450, #18447, #18442, #18327, #18395, #18418, #18350, #18278, #18242, #18234, #18203, #18175, #18210, @harupy; #18625, #18424, #18028, @daniellok-db; #18616, #18615, #18607, #18598, #18588, #18586, #18584, #18572, #18580, #18571, #18554, #18553, #18552, #18551, #18548, #18546, #18528, #18527, #18525, #18521, #18520, #18515, #18519, #18518, #18506, #18507, #18505, #18502, #18495, #18494, #18472, #18463, #18464, #18462, #18443, #18440, #18399, #18394, #18393, #18392, #18390, #18389, #18380, #18376, #18378, #18377, #18366, #18362, #18361, #18343, #18340, #18318, #18311, #18307, #18269, #18268, #18261, #18260, #18259, #18258, #18257, #18256, #18253, #18254, #18252, #18250, #18243, #18238, #18213, #18206, #18198, #18184, #18179, @copilot-swe-agent; #18578, #18569, @TomeHirata; #18575, @dbrx-euirim; #18570, #18116, #18360, #18351, @WeichenXu123; #18513, #18461, #18430, #18336, @BenWilson2; #18459, @smoorjani; #18488, @raymondzhou-db; #18334, @NJAHNAVI2907

## 3.6.0rc0 (2025-11-03)

MLflow 3.6.0rc0 includes several major features and improvements!

- Full OpenTelemetry Support in OSS Server
- Session-level View in Trace UI
- New experiment tab bar
- Vercel AI Support in TypeScript Tracing SDK
- Tracking Judge Cost and Traces
- Agent Server

Stay tuned for the full release, which will be packed with more features and bugfixes.

To try out this release candidate, please run:

`pip install mlflow==3.6.0rc0`

## 3.5.1 (2025-10-21)

MLflow 3.5.1 is a patch release that includes several bug fixes and improvements.

Features:

- [CLI] Add CLI command to list registered scorers by experiment (#18255, @alkispoly-db)
- [Deployments] Add configuration option for long-running deployments client requests (#18363, @BenWilson2)
- [Deployments] Create `set_databricks_monitoring_sql_warehouse_id` API (#18346, @dbrx-euirim)
- [Prompts] Show instructions for prompt optimization on prompt registry (#18375, @TomeHirata)

Bug fixes:

- [Evaluation] Validate if trace is None before accessing the value in mlflow.genai.evaluate (#18285, @srinathmkce)
- [Evaluation] Revert "Add atomicity to job_start API (#18226)" (@serena-ruan)
- [MCP] Move fastmcp to optional mcp extra (#18422, @harupy)
- [Model Registry] Fix serialization bug in file store (#18365, @BenWilson2)
- [Scoring] Pin uvloop<0.22 to fix mlserver compatibility (#18370, @harupy)
- [Tracing] Fix a forward-compatibility issue with Span to_dict (#18439, @serena-ruan)
- [Tracing] Whitelist notebook trace UI renderer to allow display with default security settings (#18446, @TomeHirata)
- [Tracing] Fix attribute error in StrandsAgent tracing (#18409, @B-Step62)
- [Tracing] Adjust truncation logic in trace previews (#18412, @BenWilson2)
- [Tracing] Revert "Fix response handling in log_spans (#18280)" (#18349, @serena-ruan)
- [Tracking] Adjust util for remote tracking server declaration (#18411, @BenWilson2)
- [Tracking] Handle Databricks FMAPI style in openai autolog (#18354, @TomeHirata)
- [Tracking] Fetch config after adding first record (#18338, @serena-ruan)
- [UI] Fix span ID parsing in the UI (#18419, @daniellok-db)
- [UI] Fix Chat message parsing within the trace summary view modal (#18454, @daniellok-db)
- [UI] Fix an issue with display of the assessments pane in the UI (#18333, @BenWilson2)

Documentation updates:

- [Docs] Fix Kubernetes Deployment Tutorial Code (#18381, @Maeril)
- [Docs] Update the documentation around requirements for optimize_prompts (#18398, @TomeHirata)
- [Docs] Fix example FastAPI in track user sessions (#18388, @maxscheijen)

## 3.5.0 (2025-10-16)

MLflow 3.5.0 includes several major features and improvements!

### Major Features

- ⚙️ **Job Execution Backend**: Introduced a new job execution backend infrastructure for running asynchronous tasks with individual execution pools, job search capabilities, and transient error handling. (#17676, #18012, #18070, #18071, #18112, #18049, @WeichenXu123)
- 🎯 **Flexible Prompt Optimization API**: Introduced a new flexible API for prompt optimization with support for model switching and the GEPA algorithm, enabling more efficient prompt tuning with fewer rollouts. See the [documentation](https://mlflow.org/docs/latest/genai/prompt-registry/optimize-prompts/) to get started. (#18183, #18031, @TomeHirata)
- 🎨 **Enhanced UI Onboarding**: Improved in-product onboarding experience with trace quickstart drawer and updated homepage guidance to help users discover MLflow's latest features. (#18098, #18187, @B-Step62)
- 🔐 **Security Middleware for Tracking Server**: Added a security middleware layer to protect against DNS rebinding, CORS attacks, and other security threats. Read the [documentation](https://mlflow.org/docs/latest/self-hosting/security/network/) for configuration details. (#17910, @BenWilson2)

### Features

- [Tracing / Tracking] Add `unlink_traces_from_run` batch operation (#18316, @harupy)
- [Tracing] Add batch trace link/unlink operations to DatabricksTracingRestStore (#18295, @harupy)
- [Tracking] Claude Code SDK autologging support (#18022, @smoorjani)
- [Tracing] Add support for reading trace configuration from environment variables (#17792, @joelrobin18)
- [Tracking] Mistral tracing improvements (#16370, @joelrobin18)
- [Tracking] Gemini token count tracking (#16248, @joelrobin18)
- [Tracking] Gemini streaming support (#16249, @joelrobin18)
- [Tracking] CrewAI token count tracking with documentation updates (#16373, @joelrobin18)
- [Evaluation] Allow passing empty scorer list for manual result comparison (#18265, @B-Step62)
- [Evaluation] Log assessments to DSPy evaluation traces (#18136, @B-Step62)
- [Evaluation] Add support for trace inputs to built-in scorers (#17943, @BenWilson2)
- [Evaluation] Add synonym handling for built-in scorers (#17980, @BenWilson2)
- [Evaluation] Add span timing tool for Agent Judges (#17948, @BenWilson2)
- [Evaluation] Allow disabling evaluation sample check (#18032, @B-Step62)
- [Evaluation] Reduce verbosity of SIMBA optimizer logs when aligning judges (#17795, @BenWilson2)
- [Evaluation] Add `__repr__` method for Judges (#17794, @BenWilson2)
- [Prompts] Add prompt registry support to MLflow webhooks (#17640, @harupy)
- [Prompts] Prompt Registry Chat UI (#17334, @joelrobin18)
- [UI] Delete parent and child runs together (#18052, @joelrobin18)
- [UI] Added move to top, move to bottom for charts (#17742, @joelrobin18)
- [Tracking] Use sampling data for run comparison to improve performance (#17645, @lkuo)
- [Tracking] Add optional 'outputs' column for evaluation dataset records (#17735, @WeichenXu123)

### Bug Fixes

- [Tracing] Fix parent run resolution mechanism for LangChain (#17273, @B-Step62)
- [Tracing] Add client-side retry for `get_trace` to improve reliability (#18224, @B-Step62)
- [Tracing] Fix OpenTelemetry dual export (#18163, @B-Step62)
- [Tracing] Suppress false warnings from span logging (#18092, #18276, @B-Step62)
- [Tracing] Fix OpenTelemetry resource attributes not propagating correctly (#18019, @xiaosha007)
- [Tracing] Fix DSPy prompt display (#17988, @B-Step62)
- [Tracing] Fix usage aggregation to avoid ancestor duplication (#17921, @TomeHirata)
- [Tracing] Fix double counting in Strands tracing (#17855, @joelrobin18)
- [Tracing] Fix `to_predict_fn` to handle traces without tags field (#17784, @harupy)
- [Tracing] URL-encode trace tag keys in `delete_trace_tag` to prevent 404 errors (#18232, @copilot-swe-agent)
- [Tracking] Fix Claude Code autologging inputs not displaying (#17858, @smoorjani)
- [Tracking] Fix runs with 0-valued metrics not appearing in experiment list contour plots (#17916, @WeichenXu123)
- [Tracking] Fix DSPy run display (#18137, @B-Step62)
- [Tracking] Allow list of types in tools JSON Schema for OpenAI autolog (#17908, @fedem96)
- [Tracking] Set tracking URI environment variable for job runner (#18073, @WeichenXu123)
- [Evaluation] Add atomicity to `job_start` API (#18226, @BenWilson2)
- [Evaluation] Fix trace ingest for outputs in `merge_records()` API (#18047, @BenWilson2)
- [Evaluation] Fix judge regression (#18039, @B-Step62)
- [Evaluation] Fix judges to use non-empty user messages for Anthropic model compatibility (#17935, @dbczumar)
- [Evaluation] Fix endpoints error in judge (#18048, @joelrobin18)
- [Model Registry] Fix creating model versions from non-Databricks tracking to Databricks Unity Catalog registry (#18244, @austinwarner-8451)
- [Model Registry] Fix registry URI instantiation for artifact download (#17982, @arpitjasa-db)
- [Model Registry] Include original error details in Unity Catalog model copy failure messages (#17997, @harupy)
- [Model Registry] Fix webhook delivery to exit early for FileStore instances (#18015, @copilot-swe-agent)
- [Prompts] Fix error suppression during prompt alias resolution when `allow_missing` is set (#17541, @mr-brobot)
- [UI] General UI improvements (#18281, @joelrobin18)
- [Models] Fix dataset issue (#18081, @joelrobin18)
- [Models] Forward dataset name and digest to PolarsDataset's `to_evaluation_dataset` method (#17886, @sadelcarpio)
- [Build] Fix `mlflow server` exiting immediately when optional `huey` package is missing (#18016, @harupy)
- [Scoring] Fix chat completion arguments (#18248, @aravind-segu)

### Documentation Updates

- [Docs] Add self-hosted documentation support (#17986, @B-Step62)
- [Docs] Add GitHub feature requests section to GenAI documentation (#18342, @TomeHirata)
- [Docs] Update Claude Code SDK tracing documentation (#18026, @smoorjani)
- [Docs] Add documentation for Analyze Experiment MCP/CLI command (#17978, @nsthorat)
- [Docs] Add deprecation notice for custom prompt judge (#18287, @smoorjani)
- [Docs] Overhaul scorer documentation (#17930, @B-Step62)
- [Docs] Add default optimizer documentation (#17814, @BenWilson2)
- [Docs] Update TypeScript SDK contribution documentation (#17995, @joelrobin18)
- [Docs] Fix Postgres 18+ mount path in documentation (#18192, @soyun11)
- [Docs] Fix typo: correct variable name from `max_few_show_examples` to `max_few_shot_examples` (#18246, @srinathmkce)
- [Docs] Replace single quotes with double quotes for Windows compatibility (#18266, @PavithraNelluri)
- [Docs] Fix typo in model registry documentation (#18038, @EddieMG)

Small bug fixes and documentation updates:

#18349, #18338, #18241, #18319, #18309, #18292, #18280, #18239, #18236, #17786, #18003, #17970, #17898, #17765, #17667, @serena-ruan; #18346, #17882, @dbrx-euirim; #18306, #18208, #18165, #18110, #18109, #18108, #18107, #18105, #18104, #18100, #18099, #18155, #18079, #18082, #18078, #18077, #18083, #18030, #18001, #17999, #17712, #17785, #17756, #17729, #17731, #17733, @daniellok-db; #18339, #18291, #18222, #18210, #18124, #18101, #18054, #18053, #18007, #17922, #17823, #17822, #17805, #17789, #17750, #17752, #17760, #17758, #17688, #17689, #17693, #17675, #17673, #17656, #17674, @harupy; #18331, #18308, #18303, #18146, @smoorjani; #18315, #18279, #18310, #18187, #18225, #18277, #18193, #18223, #18209, #18200, #18178, #17574, #18021, #18006, #17944, @B-Step62; #18290, #17946, #17627, @bbqiu; #18274, @Ninja3047; #18204, #17868, #17866, #17833, #17826, #17835, @TomeHirata; #18273, #18043, #17928, #17931, #17936, #17937, @dbczumar; #18185, #18180, #18174, #18170, #18167, #18164, #18168, #18166, #18162, #18160, #18159, #18157, #18156, #18154, #18148, #18145, #18135, #18143, #18142, #18139, #18132, #18130, #18119, #18117, #18115, #18102, #18075, #18046, #18062, #18042, #18051, #18036, #18027, #18014, #18011, #18009, #18004, #17903, #18000, #18002, #17973, #17993, #17989, #17984, #17968, #17966, #17967, #17962, #17977, #17976, #17972, #17965, #17964, #17963, #17969, #17971, #17939, #17926, #17924, #17915, #17911, #17912, #17904, #17902, #17900, #17897, #17892, #17889, #17888, #17885, #17884, #17878, #17874, #17873, #17871, #17870, #17865, #17860, #17861, #17859, #17857, #17856, #17854, #17853, #17851, #17849, #17850, #17847, #17845, #17846, #17844, #17843, #17842, #17838, #17836, #17834, #17831, #17824, #17828, #17819, #17825, #17817, #17821, #17809, #17807, #17808, #17803, #17800, #17799, #17797, #17793, #17790, #17772, #17771, #17769, #17770, #17753, #17762, #17747, #17749, #17745, #17740, #17734, #17732, #17726, #17723, #17722, #17721, #17719, #17720, #17718, #17716, #17713, #17715, #17710, #17709, #17708, #17707, #17705, #17697, #17701, #17698, #17696, #17695, @copilot-swe-agent; #18151, #18153, #17983, #18040, #17981, #17841, #17818, #17776, #17781, @BenWilson2; #18068, @alkispoly-db; #18133, @kevin-lyn; #17105, #17717, @joelrobin18; #17879, @lkuo; #17996, #17945, #17913, @WeichenXu123

## 3.5.0rc0 (2025-10-08)

MLflow 3.5.0rc0 includes several major features and improvements

Major new features:

- 🤖 **Tracing support for Claude Code SDK**: MLflow now provides a tracing integration for both the Claude Code CLI and SDK! Configure the autologging integration to track your prompts, Claude's responses, tool calls, and more. Check out this [doc page](https://mlflow.org/docs/latest/genai/tracing/integrations/listing/claude_code/) to get started. (#18022, @smoorjani)
- ✨ **Improved UI homepage**: The MLflow UI's homepage has been updated to help you get started with more of our latest features. This page will be updated regularly moving forward, allowing you to get more in-product guidance.
- 🗂️ **Evaluation datasets UI integration**: In MLflow 3.4.0, we released backend support for creating evaluation datasets for GenAI applications. In this release, we've added a new tab to the MLflow Experiment UI, allowing you to create, manage, and export traces to your datasets without having to write a line of code.
- 🧮 **GEPA support for prompt optimization**: MLflow's prompt optimization feature now supports the [GEPA algorithm](https://dspy.ai/api/optimizers/GEPA/overview/), allowing you to achieve higher performing prompts with less rollouts. For instructions on how to get started with prompt optimization, visit this [doc page](https://mlflow.org/docs/latest/genai/prompt-registry/optimize-prompts/)!
- 🔐 **Security middleware layer for tracking server**: MLflow now ships with a security middleware layer by default, allowing you to protect against DNS rebinding, CORS attacks, and more. Read the documentation [here](https://mlflow.org/docs/latest/self-hosting/security/network/) to learn how to configure these options.

Stay tuned for the full release, which will be packed with more features and bugfixes.

To try out this release candidate, please run:

`pip install mlflow==3.5.0rc0`

## 3.4.0rc0 (2025-09-11)

MLflow 3.4.0rc0 includes several major features and improvements

### Major New Features

- 📊 **OpenTelemetry Metrics Export**: MLflow now exports span-level statistics as OpenTelemetry metrics, providing enhanced observability and monitoring capabilities for traced applications. (#17325, @dbczumar)
- 🤖 **MCP Server Integration**: Introducing the Model Context Protocol (MCP) server for MLflow, enabling AI assistants and LLMs to interact with MLflow programmatically. (#17122, @harupy)
- 🧑‍⚖️ **Custom Judges API**: New `make_judge` API enables creation of custom evaluation judges for assessing LLM outputs with domain-specific criteria. (#17647, @BenWilson2, @dbczumar, @alkispoly-db, @smoorjani)
- 📈 **Correlations Backend**: Implemented backend infrastructure for storing and computing correlations between experiment metrics using NPMI (Normalized Pointwise Mutual Information). (#17309, #17368, @BenWilson2)
- 🗂️ **Evaluation Datasets**: MLflow now supports storing and versioning evaluation datasets directly within experiments for reproducible model assessment. (#17447, @BenWilson2)
- 🔗 **Databricks Backend for MLflow Server**: MLflow server can now use Databricks as a backend, enabling seamless integration with Databricks workspaces. (#17411, @nsthorat)
- 🤖 **Claude Autologging**: Automatic tracing support for Claude AI interactions, capturing conversations and model responses. (#17305, @smoorjani)
- 🌊 **Strands Agent Tracing**: Added comprehensive tracing support for Strands agents, including automatic instrumentation for agent workflows and interactions. (#17151, @joelrobin18)
- 🧪 **Experiment Types in UI**: MLflow now introduces experiment types, helping reduce clutter between classic ML/DL and GenAI features. MLflow auto-detects the type, but you can easily adjust it via a selector next to the experiment name. (#17605, @daniellok-db)

Features:

- [Evaluation] Add ability to pass tags via dataframe in mlflow.genai.evaluate (#17549, @smoorjani)
- [Evaluation] Add custom judge model support for Safety and RetrievalRelevance builtin scorers (#17526, @dbrx-euirim)
- [Tracing] Add AI commands as MCP prompts for LLM interaction (#17608, @nsthorat)
- [Tracing] Add MLFLOW_ENABLE_OTLP_EXPORTER environment variable (#17505, @dbczumar)
- [Tracing] Support OTel and MLflow dual export (#17187, @dbczumar)
- [Tracing] Make set_destination use ContextVar for thread safety (#17219, @B-Step62)
- [CLI] Add MLflow commands CLI for exposing prompt commands to LLMs (#17530, @nsthorat)
- [CLI] Add 'mlflow runs link-traces' command (#17444, @nsthorat)
- [CLI] Add 'mlflow runs create' command for programmatic run creation (#17417, @nsthorat)
- [CLI] Add MLflow traces CLI command with comprehensive search and management capabilities (#17302, @nsthorat)
- [CLI] Add --env-file flag to all MLflow CLI commands (#17509, @nsthorat)
- [Tracking] Backend for storing scorers in MLflow experiments (#17090, @WeichenXu123)
- [Model Registry] Allow cross-workspace copying of model versions between WMR and UC (#17458, @arpitjasa-db)
- [Models] Add automatic Git-based model versioning for GenAI applications (#17076, @harupy)
- [Models] Improve WheeledModel.\_download_wheels safety (#17004, @serena-ruan)
- [Projects] Support resume run for Optuna hyperparameter optimization (#17191, @lu-wang-dl)
- [Scoring] Add MLFLOW_DEPLOYMENT_CLIENT_HTTP_REQUEST_TIMEOUT environment variable (#17252, @dbczumar)
- [UI] Add ability to hide/unhide all finished runs in Chart view (#17143, @joelrobin18)
- [Telemetry] Add MLflow OSS telemetry for invoke_custom_judge_model (#17585, @dbrx-euirim)

Bug fixes:

- [Evaluation] Implement DSPy LM interface for default Databricks model serving (#17672, @smoorjani)
- [Evaluation] Fix aggregations incorrectly applied to legacy scorer interface (#17596, @BenWilson2)
- [Evaluation] Add Unity Catalog table source support for mlflow.evaluate (#17546, @BenWilson2)
- [Evaluation] Fix custom prompt judge encoding issues with custom judge models (#17584, @dbrx-euirim)
- [Tracking] Fix OpenAI autolog to properly reconstruct Response objects from streaming events (#17535, @WeichenXu123)
- [Tracking] Add basic authentication support in TypeScript SDK (#17436, @kevin-lyn)
- [Tracking] Update scorer endpoints to v3.0 API specification (#17409, @WeichenXu123)
- [Tracking] Fix scorer status handling in MLflow tracking backend (#17379, @WeichenXu123)
- [Tracking] Fix missing source-run information in UI (#16682, @WeichenXu123)
- [Scoring] Fix spark_udf to always use stdin_serve for model serving (#17580, @WeichenXu123)
- [Scoring] Fix a bug with Spark UDF usage of uv as an environment manager (#17489, @WeichenXu123)
- [Model Registry] Extract source workspace ID from run_link during model version migration (#17600, @arpitjasa-db)
- [Models] Improve security by reducing write permissions in temporary directory creation (#17544, @BenWilson2)
- [Server-infra] Fix --env-file flag compatibility with --dev mode (#17615, @nsthorat)
- [Server-infra] Fix basic authentication with Uvicorn server (#17523, @kevin-lyn)
- [UI] Fix experiment comparison functionality in UI (#17550, @Flametaa)
- [UI] Fix compareExperimentsSearch route definitions (#17459, @WeichenXu123)

Documentation updates:

- [Docs] Add clarification for trace requirements in scorers documentation (#17542, @BenWilson2)
- [Docs] Add documentation for Claude code autotracing (#17521, @smoorjani)
- [Docs] Remove experimental status message for MPU/MPD features (#17486, @BenWilson2)
- [Docs] Remove problematic pages from documentation (#17453, @BenWilson2)
- [Docs] Add documentation for updating signatures on Databricks registered models (#17450, @arpitjasa-db)
- [Docs] Update Scorers API documentation (#17298, @WeichenXu123)
- [Docs] Add comprehensive documentation for scorers (#17258, @B-Step62)

Small bug fixes and documentation updates:

#17655, #17657, #17597, #17545, #17547, @BenWilson2; #17671, @smoorjani; #17668, #17665, #17662, #17661, #17659, #17658, #17653, #17643, #17642, #17636, #17634, #17631, #17628, #17611, #17607, #17588, #17570, #17575, #17564, #17557, #17556, #17555, #17536, #17531, #17524, #17510, #17511, #17499, #17500, #17494, #17493, #17490, #17488, #17478, #17479, #17425, #17471, #17457, #17440, #17403, #17405, #17404, #17402, #17366, #17346, #17344, #17337, #17316, #17313, #17284, #17276, #17235, #17226, #17229, @copilot-swe-agent; #17664, #17654, #17613, #17637, #17633, #17612, #17630, #17616, #17626, #17617, #17610, #17614, #17602, #17538, #17522, #17512, #17508, #17492, #17462, #17475, #17468, #17455, #17338, #17257, #17231, #17214, #17223, #17218, #17216, @harupy; #17635, #17663, #17426, #16870, #17428, #17427, #17441, #17377, @serena-ruan; #17605, #17306, @daniellok-db; #17624, #17578, #17369, #17391, #17072, #17326, #17115, @dbczumar; #17598, #17408, #17353, @nsthorat; #17601, #17553, @dbrx-euirim; #17586, #17587, #17310, #17180, @TomeHirata; #17516, @bbqiu; #17477, #17474, @WeichenXu123; #17449, @raymondzhou-db; #17470, @jacob-danner; #17378, @arpitjasa-db; #17121, @ctaymor; #17351, #17322, @ispoljari; #17292, @dsuhinin; #17287, #17281, #17230, #17245, #17237, @B-Step62

## 3.3.2 (2025-08-27)

MLflow 3.3.2 is a patch release that includes several minor improvements and bugfixes

Features:

- [Evaluation] Add support for dataset name persistence (#17250, @BenWilson2)

Bug fixes:

- [Tracing] Add retry policy support to `_invoke_litellm` for improved reliability (#17394, @dbczumar)
- [UI] fix ui sorting in experiments (#17340, @Flametaa)
- [Serving] Add Databricks Lakebase Resource (#17277, @jennsun)
- [Tracing] Fix set trace tags endpoint (#17362, @daniellok-db)

Documentation updates:

- [Docs] Add docs for package lock (#17395, @BenWilson2)
- [Docs] Fix span processor docs (#17386, @mr-brobot)

Small bug fixes and documentation updates:

#17301, #17299, @B-Step62; #17420, #17421, #17398, #17397, #17349, #17361, #17377, #17359, #17358, #17356, #17261, #17263, #17262, @serena-ruan; #17422, #17310, #17357, @TomeHirata; #17406, @sotagg; #17418, @annzhang-db; #17384, #17376, @daniellok-db

## 3.3.1 (2025-08-20)

MLflow 3.3.1 includes several major features and improvements

Bug fixes:

- [Tracking] Fix `mlflow.genai.datasets` attribute (#17307, @WeichenXu123)
- [UI] Fix tag display as column in experiment overview (#17296, @joelrobin18)
- [Tracing] Fix the slowness of dspy tracing (#17290, @TomeHirata)

Small bug fixes and documentation updates:

#17295, @gunsodo; #17272, @bbqiu

## 3.3.0 (2025-08-19)

MLflow 3.3.0 includes several major features and improvements

### Major new features:

- 🪝 **Model Registry Webhooks**: MLflow now supports [webhooks](https://mlflow.org/docs/latest/ml/webhooks/) for model registry events, enabling automated notifications and integrations with external systems. (#16583, @harupy)
- 🧭 **Agno Tracing Integration**: Added [Agno tracing integration](https://mlflow.org/docs/latest/genai/tracing/integrations/listing/agno/) for enhanced observability of AI agent workflows. (#16995, @joelrobin18)
- 🧪 **GenAI Evaluation in OSS**: MLflow open-sources [the new evaluation capability for LLM applications](https://mlflow.org/docs/latest/genai/eval-monitor/). This suite enables systematic measurement and improvement of LLM application quality, with tight integration into MLflow's observability, feedback collection, and experiment tracking capabilities. (#17161, #17159, @B-Step62)
- 🖥️ **Revamped Trace Table View**: The new trace view in MLflow UI provides a streamlined interface for exploring, filtering, and monitoring traces, with enhanced search capabilities including full-text search across requests.(#17092, @daniellok-db)
- ⚡️ **FastAPI + Uvicorn Server**: MLflow Tracking Server now defaults to FastAPI + Uvicorn for improved performance, while maintaining Flask compatibility. (#17038, @dbczumar)

New features:

- [Tracking] Add a Docker compose file to quickly start a local MLflow server with recommended minimum setup (#17065, @joelrobin18)
- [Tracing] Add `memory` span type for agentic workflows (#17034, @B-Step62)
- [Prompts] Enable custom prompt optimizers in `optimize_prompt` including DSPy support (#17052, @TomeHirata)
- [Model Registry / Prompts] Proper support for the @latest alias (#17146, @B-Step62)
- [Metrics] Allow custom tokenizer encoding in `token_count` function (#16253, @joelrobin18)

Bug fixes:

- [Tracking] Fix Databricks secret scope check to reduce audit log errors (#17166, @harupy)
- [Tracking] Fix Databricks SDK error code mapping in retry logic (#17095, @harupy)
- [Tracking] Fix Databricks secret scope check to reduce error rates (#17166, @harupy)
- [Tracing] Remove API keys from CrewAI traces to prevent credential leakage (#17082, @diy2learn)
- [Tracing] Fix LiteLLM span association issue by making callbacks synchronous (#16982, @B-Step62)
- [Tracing] Fix OpenAI Agents tracing (#17227, @B-Step62)
- [Evaluation] Fix issue with get_label_schema has no attribute (#17163, @smoorjani)
- [Docs] Fix version selector on API Reference page by adding missing CSS class and versions.json generation (#17247, @copilot-swe-agent)

Documentation updates:

- [Docs] Document custom optimizer usage with `optimize_prompt` (#17084, @TomeHirata)
- [Docs] Fix built-in scorer documentation for expectation parameter (#17075, @smoorjani)
- [Docs] Add comprehensive documentation for scorers (#17258, @B-Step62)

Small bug fixes and documentation updates:

#17230, #17264, #17289, #17287, #17265, #17238, #17215, #17224, #17185, #17148, #17193, #17157, #17067, #17033, #17087, #16973, #16875, #16956, #16959, @B-Step62; #17269, @BenWilson2; #17285, #17259, #17260, #17236, #17196, #17169, #17062, #16943, @serena-ruan; #17253, @sotagg; #17212, #17206, #17211, #17207, #17205, #17118, #17177, #17182, #17170, #17153, #17168, #17123, #17136, #17119, #17125, #17088, #17101, #17056, #17077, #17057, #17036, #17018, #17024, #17019, #16883, #16972, #16961, #16968, #16962, #16958, @harupy; #17209, #17202, #17184, #17179, #17174, #17141, #17155, #17145, #17130, #17113, #17110, #17098, #17104, #17100, #17060, #17044, #17032, #17008, #17001, #16994, #16991, #16984, #16976, @copilot-swe-agent; #17069, @hayescode; #17199, #17081, #16928, #16931, @TomeHirata; #17198, @WeichenXu123; #17195, #17192, #17131, #17128, #17124, #17120, #17102, #17093, #16941, @daniellok-db; #17070, #17074, #17073, @dbczumar

## 3.2.0 (2025-08-05)

MLflow 3.2.0 includes several major features and improvements

### Major New Features

- 🧭 **Tracing TypeScript SDK**: MLflow Tracing now supports the [TypeScript SDK](https://github.com/mlflow/mlflow/tree/master/libs/typescript), allowing developers to trace GenAI applications in TypeScript environments. (#16871, @B-Step62)
- 🔗 **Semantic Kernel Tracing**: MLflow now provides [automatic tracing support for Semantic Kernel](https://mlflow.org/docs/latest/genai/tracing/integrations/listing/semantic_kernel/), simplifying trace capture for SK-based workflows. (#16469, @michael-berk)
- 🧪 **Feedback Tracking**: MLflow OSS now natively supports tracking [human feedbacks](https://mlflow.org/docs/latest/genai/assessments/feedback/), [ground truths](https://mlflow.org/docs/latest/genai/assessments/expectations/), LLM judges on traces, providing integrated quality monitoring and feedback management capabilities. (#16743, @BenWilson2)
- 🖥️ **MLflow UI Improvements**: The MLflow UI now features **a redesigned experiment home view** and includes enhancements like pagination on the model page for better usability. (#16464, @frontsideair, #15801, @Flametaa)
- 🔍 **Updated Trace UI**: The Trace UI now has image support when rendering chat messages for OpenAI, Langchain, and Anthropic! Additionally, we're introducing a "summary view" which is a simplified, flat representation of the important spans in a trace. The full detail view is still available in a separate tab.
- 🛡️ **PII Masking in Tracing**: Added support for [masking personally identifiable information (PII) via a custom span post-processor](https://mlflow.org/docs/latest/genai/tracing/observe-with-traces/masking). (#16344, @B-Step62)
- 🐻‍❄️ **Polars Dataset Support**: MLflow now supports [Polars datasets](https://mlflow.org/docs/latest/ml/dataset/#dataset), expanding compatibility with performant DataFrame libraries. (#13006, @AlpAribal)

### 📊 Usage Tracking (New in 3.2.0)

- Starting with version 3.2.0, MLflow will begin collecting anonymized usage data about how core features of the platform are used. This data contains **no sensitive or personally identifiable information**, and users can opt out of data collection at any time. Check [MLflow documentation](https://mlflow.org/docs/latest/community/usage-tracking/) for more details. (#16439, @serena-ruan)

Features:

- [Tracing] Include mlflow-tracing as a dependency of mlflow (#16589, @B-Step62)
- [Tracing] Convert DatabricksRM output to MLflow document format (#16866, @WeichenXu123)
- [Tracing] Add unified token usage tracking for Bedrock LLMs (#16351, @mohammadsubhani)
- [Tracing] Token usage tracking for agent frameworks including Anthropic, Autogen, LlamaIndex etc. (#16251, #16362, #16246, #16258, #16313, #16312, #16340, #16357, #16358, @joelrobin18, #16387, @sanatb187)
- [Tracing] Render multi-modal trace for LangChain (#16799, @B-Step62)
- [Tracing] Support async tracing for Gemini (#16632, @B-Step62)
- [Tracing] Support global sampling for tracing (#16700, @B-Step62)
- [Tracing] ResponsesAgent tracing aggregation (#16787, @bbqiu)
- [Tracing] Add Agent and LLM complete name (#16613, @joelrobin18)
- [Tracking] Allow setting thread-local tracing destination via mlflow.tracing.set_destination (#16859, @WeichenXu123)
- [Tracking] Introduce MLFLOW_DISABLE_SCHEMA_DETAILS environment variable to toggle detailed schema errors (#16631, @NJAHNAVI2907)
- [Tracking] Add support for chat-style prompts with structured output with prompt object (#16341, @harshilprajapati96)
- [Tracking] Add support for responses.parse calls in oai autologger (#16245, @dipakkrishnan)
- [Tracking] Add support for uv as an environment manager in mlflow run (#16274, @isuyyy)
- [Evaluation] Replace guideline_adherence to guidelines (#16856, @smoorjani)
- [Evaluation] Replace Scheduled Scorers API to a Scorer Registration System (#16977, @dbrx-euirim)
- [UI] Add tag filter to the experiments page (#16648, @frontsideair)
- [UI] Add ability to the UI to edit experiment tags (#16614, @frontsideair)
- [UI] Create runs table using selected columns in the experiment view (#16804, @wangh118)
- [Scoring] Make spark_udf support 'uv' env manager (#16292, @WeichenXu123)

Bug fixes:

- [Tracking / UI] Add missing default headers and replace absolute URLs in new browser client requests (GraphQL & logged models) (#16840, @danilopeixoto)
- [Tracking] Fix tracking_uri positional argument bug in artifact repositories (#16878, @copilot-swe-agent)
- [Models] Fix UnionType support for Python 3.10 style union syntax (#16882, @harupy)
- [Tracing / Tracking] Fix OpenAI autolog Pydantic validation for enum values (#16862, @mohammadsubhani)
- [Tracking] Fix tracing for Anthropic and Langchain combination (#15151, @maver1ck)
- [Models] Fix OpenAI multimodal message logging support (#16795, @mohammadsubhani)
- [Tracing] Avoid using nested threading for Azure Databricks trace export (#16733, @TomeHirata)
- [Evaluation] Bug fix: Databricks GenAI evaluation dataset source returns string, instead of DatasetSource instance (#16712, @dbczumar)
- [Models] Fix `get_model_info` to provide logged model info (#16713, @harupy)
- [Evaluation] Fix serialization and deserialization for python scorers (#16688, @connorchenn)
- [UI] Fix GraphQL handler erroring on NaN metric values (#16628, @daniellok-db)
- [UI] Add back video artifact preview (#16620, @daniellok-db)
- [Tracing] Proper chat message reconstruction from OAI streaming response (#16519, @B-Step62)
- [Tracing] Convert trace column in search_traces() response to JSON string (#16523, @B-Step62)
- [Evaluation] Fix mlflow.evaluate crashes in \_get_binary_classifier_metrics due to … (#16485, @mohammadsubhani)
- [Evaluation] Fix trace detection logic for `mlflow.genai.evaluate` (#16932, @B-Step62)
- [Evaluation] Enable to use make_genai_metric_from_prompt for mlflow.evaluate (#16960, @TomeHirata)
- [Models] Add explicit encoding for decoding streaming Responses (#16855, @aravind-segu)
- [Tracking] Prevent from tracing DSPy model API keys (#17021, @czyzby)
- [Tracking] Fix pytorch datetime issue (#17030, @serena-ruan)
- [Tracking] Fix predict with pre-releases (#16998, @serena-ruan)

Documentation updates:

- [Docs] Overhaul of top level version management GenAI docs (#16728, @BenWilson2)
- [Docs] Fix Additional GenAI Docs pages (#16691, @BenWilson2)
- [Docs] Update the docs selector dropdown (#16280, @BenWilson2)
- [Docs] Update docs font sizing and link coloring (#16281, @BenWilson2)
- [Docs] Fix typo in model deployment page (#16999, @premkiran-o7)

Small bug fixes and documentation updates:

#17003, #17049, #17035, #17026, #16981, #16971, #16953, #16930, #16917, #16738, #16717, #16693, #16694, #16684, #16678, #16656, #16513, #16459, #16277, #16276, #16275, #16170, #16217, @serena-ruan; #16927, #16915, #16913, #16911, #16909, #16889, #16727, #16600, #16543, #16551, #16526, #16533, #16535, #16531, #16472, #16392, #16389, #16385, #16376, #16369, #16367, #16321, #16311, #16307, #16273, #16268, #16265, #16112, #16243, #16231, #16226, #16221, #16196, @copilot-swe-agent; #17050, #17048, #16955, #16894, #16885, #16860, #16841, #16835, #16801, #16701, @daniellok-db; #16898, #16881, #16858, #16735, #16823, #16814, #16647, #16750, #16809, #16794, #16793, #16789, #16780, #16770, #16773, #16771, #16772, #16768, #16752, #16754, #16751, #16748, #16730, #16729, #16346, #16709, #16704, #16703, #16702, #16658, #16662, #16645, #16639, #16640, #16626, #16572, #16566, #16565, #16563, #16561, #16559, #16544, #16539, #16520, #16508, #16505, #16494, #16495, #16491, #16487, #16482, #16473, #16465, #16456, #16458, #16394, #16445, #16433, #16434, #16413, #16417, #16416, #16414, #16415, #16378, #16350, #16323, #15788, #16263, #16256, #16237, #16234, #16219, #16216, #16207, #16199, #16192, #16705, @harupy; #17047, #17017, #17005, #16989, #16952, #16951, #16903, #16900, #16755, #16762, #16757, #15860, #16661, #16630, #16657, #16605, #16602, #16568, #16569, #16553, #16345, #16454, #16489, #16486, #16438, #16266, #16382, #16381, #16303, @B-Step62; #17028, #17027, #17020, @he7d3r; #16969, #16957, #16852, #16829, #16816, #16808, #16775, #16807, #16806, #16624, #16524, #16410, #16403, @TomeHirata; #16987, @wangh118; #16760, #16761, #16736, #16737, #16699, #16718, #16663, #16676, #16574, #16477, #16552, #16527, #16515, #16452, #16210, #16204, #16610, @frontsideair; #16723, #16124, @AveshCSingh; #16744, @BenWilson2; #16683, @dsuhinin; #16877, #16502, @bbqiu; #16619, @AchimGaedkeLynker; #16595, @Aiden-Jeon; #16480, #16479, @shushantrishav; #16398, #16331, #16328, #16329, #16293, @WeichenXu123

## 3.1.4 (2025-07-23)

MLflow 3.1.4 includes several major features and improvements

Small bug fixes and documentation updates:

#16835, #16820, @daniellok-db

## 3.1.3 (2025-07-22)

MLflow 3.1.3 includes several major features and improvements

Features:

- [Artifacts / Tracking] Do not copy file permissions when logging artifacts to local artifact repo (#16642, @connortann)
- [Tracking] Add support for OpenAI ChatCompletions parse method (#16493, @harupy)

Bug fixes:

- [Deployments] Propagate `MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT` to databricks-sdk (#16783, @bbqiu)
- [Model Registry] Fix issue with search_registered_models with Databricks UC backend not supporting filter_string (#16766, @BenWilson2)
- [Evaluation] Bug fix: Databricks GenAI evaluation dataset source returns string, instead of DatasetSource instance (#16712, @dbczumar)
- [Tracking] Fix the position of added tracking_uri param to artifact store implementations (#16653, @BenWilson2)

Small bug fixes and documentation updates:

#16786, #16692, @daniellok-db; #16594, @ngoduykhanh; #16475, @harupy

## 3.1.2 (2025-07-08)

MLflow 3.1.2 is a patch release that includes several bug fixes.

Bug fixes:

- [Tracking] Fix `download_artifacts` ignoring `tracking_uri` parameter (#16461, @harupy)
- [Models] Fix event type for ResponsesAgent error (#16427, @bbqiu)
- [Models] Remove falsey chat conversion for LangGraph models (#16601, @B-Step62)
- [Tracing] Use empty Resource when instantiating OTel provider to fix LiteLLM tracing issue (#16590, @B-Step62)

Small fixes and documentation updates:

#16568, #16454, #16617, #16605, #16569, #16553, #16625, @B-Step62; #16571, #16552, #16452, #16395, #16446, #16420, #16447, #16554, #16515, @frontsideair; #16558, #16443, #16457, @16442, #16449, @harupy; #16509, #16512, #16524, #16514, #16607, @TomeHirata; #16541, @copilot-swe-agent; #16427, @bbqiu; #16573, @daniellok-db; #16470, #16281, @BenWilson2

## 3.1.1 (2025-06-25)

MLflow 3.1.1 includes several major features and improvements

Features:

- [Model Registry / Sqlalchemy] Increase prompt text limit from 5K to 100K (#16377, @harupy)
- [Tracking] Support pagination in get-history of FileStore and SqlAlchemyStore (#16325, @TomeHirata)

Bug fixes:

- [Artifacts] Support downloading logged model artifacts (#16356, @TomeHirata)
- [Models] Fix bedrock provider, configured inference profile compatibility (#15604, @lloydhamilton)
- [Tracking] Specify attribute.run_id when search_traces filters by run_id (#16295, @artjen)
- [Tracking] Fix graphql batching attacks (#16227, @serena-ruan)
- [Model Registry] Make the chunk size configurable in DatabricksSDKModelsArtifactRepository (#16247, @TomeHirata)

Documentation updates:

- [Docs] Move the Lighthouse main signup page to GenAI (#16404, @BenWilson2)
- [Docs] [DOC-FIX] Dspy doc fix (#16397, @joelrobin18)
- [Docs] Fix(docs): Resolve self-referencing 'Next' link on GenAI Tracing overview page (#16334, @mohammadsubhani)
- [Docs] Update the docs selector dropdown (#16280, @BenWilson2)
- [Docs] Update utm_source for source tracking to signup URL (#16316, @BenWilson2)
- [Docs] Fix footer rendering in docs for light mode display (#16214, @BenWilson2)

Small bug fixes and documentation updates:

#16261, @rohitarun-db; #16411, #16352, #16327, #16324, #16279, #16193, #16197, @harupy; #16409, #16348, #16347, #16290, #16286, #16283, #16271, #16223, @TomeHirata; #16326, @mohammadsubhani; #16364, @BenWilson2; #16308, #16218, @serena-ruan; #16262, @raymondzhou-db; #16191, @copilot-swe-agent; #16212, @B-Step62; #16208, @frontsideair; #16205, #16200, #16198, @daniellok-db

## 3.0.1 (2025-06-25)

MLflow 3.0.1 includes several major features and improvements

Features:

- [Model Registry / Sqlalchemy] Increase prompt text limit from 5K to 100K (#16377, @harupy)

Bug fixes:

- [Models] Fix bedrock provider, configured inference profile compatibility (#15604, @lloydhamilton)

Small bug fixes and documentation updates:

#16364, @BenWilson2; #16347, @TomeHirata; #16279, #15835, @harupy; #16182, @B-Step62

## 3.1 (2025-06-11)

MLflow 3 includes several major features and improvements

Features:

- [Tracking] MLflow 3.0 (#13211, @harupy)
- [Prompts] Add Custom Prompt Judges to `mlflow[databricks]` (#16097, @dbrx-euirim)
- [Artifacts / Model Registry / Tracking] Package model environment when registering model (#15783, @qyc)
- [Tracking] Add `MlflowSparkStudy` (#15418, @lu-wang-dl)
- [Scoring] Make `spark_udf` support DBConnect + DBR 15.4 / DBR dedicated cluster (#15968, @WeichenXu123)
- [Tracking] Lock model dependencies when logging a model using `uv` (#15875, @harupy)
- [Model Registry] Introduce `mlflow.genai.optimize_prompt` to optimize prompts (#15861, @TomeHirata)
- [Tracing] Support custom request/response preview (#15919, @B-Step62)
- [Tracking] Add integration for AutoGen > 0.4 (#14729, @TomeHirata)
- [Tracking] Support token tracking for OpenAI (#15870, @B-Step62)
- [Tracking] Support tracing `ResponsesAgent.predict_stream` (#15762, @bbqiu)
- [Tracking] Introduce client and fluent APIs for `LogLoggedModelParams` (#15717, @artjen)
- [Models] Support `predict_stream` in DSPy flavor (#15678, @TomeHirata)
- [Tracking] Record notebook and git metadata in trace metadata (#15650, @B-Step62)
- [Model Registry] Added `search_prompts` function to list all the prompts registered (#15445, @joelrobin18)
- [Models] Support compression for pyfunc log model (#14700, @antbbn)
- [Gateway] Add support for Gemini in AI Gateway (#15069, @joelrobin18)
- [Tracing] PydanticAI Autologging (#15553, @joelrobin18)
- [Tracking] Support setting databricks auth profile by `DATABRICKS_CONFIG_PROFILE` environment variable. (#15587, @WeichenXu123)
- [Tracking] create mlflow tracing for `smolagents` (#15574, @y-okt)
- [Artifacts / UI] Support for video artifacts (#15518, @joelrobin18)
- [Model Registry] Add `allow_missing` parameter in `load_prompt` (#15371, @joelrobin18)
- [Tracking] Emit a warning for `mlflow.get_artifact_uri()` usage outside active run (#12902, @Shashank1202)

Bug fixes:

- [GenAI] Add Databricks App resource (#15867, @aravind-segu)
- [Tracking] Support json-string for inputs/expectations column in Spark Dataframe (#16011, @B-Step62)
- [Tracking] Avoid generating traces from scorers during evaluation (#16004, @B-Step62)
- [GenAI] Allow multi inputs module in DSPy (#15859, @TomeHirata)
- [Tracking] Improve error handling if tracking URI is not set when running `mlflow gc` (#11773, @oleg-z)
- [Tracking] Trace search: Avoid spawning threads for span fetching if `include_spans=False` (#15634, @dbczumar)
- [Tracking] Fix `global_guideline_adherence` (#15572, @artjen)
- [Model Registry] Log `Resources` from `SystemAuthPolicy` in `CreateModelVersion` (#15485, @aravind-segu)
- [Models] `ResponsesAgent` interface update (#15601, #15741, @bbqiu)

Breaking changes:

- [Tracking] Move prompt registry APIs under `mlflow.genai.prompts` namespace (#16174, @B-Step62)
- [Model Registry] Default URI to databricks-uc when tracking URI is databricks & registry URI is unspecified (#16135, @dbczumar)
- [Tracking] Do not log SHAP explainer in `mlflow.evaluate` (#15827, @harupy)
- [Tracking] Update DataFrame schema returned from `mlflow.search_trace()` to be V3 format (#15643, @B-Step62)

Documentation updates:

- [Docs] Documentation revamp for MLflow 3.0 (#15954, @harupy)
- [Docs] Add Prompt Optimization Document Page (#15958, @TomeHirata)
- [Docs] Redesign API reference page (#15811, @besirovic)
- [Docs] MLflow 3 breaking changes list (#15716, @WeichenXu123)
- [Docs] Update Lighthouse signup and signin links (#15740, @BenWilson2)
- [Docs] Document models:/ URIs explicitly in OSS MLflow docs (#15727, @WeichenXu123)
- [Docs] Spark UDF Doc update (#15586, @WeichenXu123)

Small bug fixes and documentation updates:

#16193, #16192, #16171, #16119, #16036, #16130, #16081, #16101, #16047, #16086, #16077, #16045, #16065, #16067, #16063, #16061, #16058, #16050, #16043, #16034, #16033, #15966, #16025, #16015, #16002, #15970, #16001, #15999, #15942, #15960, #15955, #15951, #15939, #15885, #15883, #15890, #15887, #15874, #15869, #15846, #15845, #15826, #15834, #15822, #15830, #15796, #15821, #15818, #15817, #15805, #15804, #15798, #15793, #15797, #15782, #15775, #15772, #15790, #15773, #15776, #15756, #15767, #15766, #15765, #15746, #15747, #15748, #15751, #15743, #15731, #15720, #15722, #15670, #15614, #15715, #15677, #15708, #15673, #15680, #15686, #15671, #15657, #15669, #15664, #15675, #15667, #15666, #15668, #15651, #15649, #15647, #15640, #15638, #15630, #15627, #15624, #15622, #15558, #15610, #15577, #15575, #15545, #15576, #15559, #15563, #15555, #15557, #15548, #15551, #15547, #15542, #15536, #15524, #15531, #15525, #15520, #15521, #15502, #15499, #15442, #15426, #15315, #15392, #15397, #15399, #15394, #15358, #15352, #15349, #15328, #15336, #15335, @harupy; #16196, #16191, #16093, #16114, #16080, #16088, #16053, #15856, #16039, #15987, #16009, #16014, #16007, #15996, #15993, #15991, #15989, #15978, #15839, #15953, #15934, #15929, #15926, #15909, #15900, #15893, #15889, #15881, #15879, #15877, #15865, #15863, #15854, #15852, #15848, @copilot-swe-agent; #16178, #16153, #16155, #15823, #15754, #15794, #15800, #15799, #15615, #15777, #15726, #15752, #15745, #15753, #15738, #15681, #15684, #15682, #15702, #15679, #15623, #15645, #15612, #15533, #15607, #15522, @serena-ruan; #16177, #16167, #16168, #16166, #16152, #16144, #15920, #16134, #16128, #16098, #16059, #16024, #15974, #15917, #15676, #15750, @dbczumar; #16162, #16161, #16137, #16126, #16127, #16099, #16074, #16041, #16040, #16010, #15945, #15697, #15588, #15602, #15581, @rohitarun-db; #16150, #15984, #16125, #16102, #16062, #16060, #15986, #15985, #15983, #15982, #15980, #15763, @smoorjani; #16160, #16149, #16103, #15538, #16055, #16054, #16048, #16012, #16029, #16003, #15940, #15956, #15950, #15906, #15922, #15932, #15930, #15905, #15910, #15902, #15901, #15840, #15896, #15898, #15895, #15850, #15833, #15824, #15819, #15816, #15806, #15803, #15795, #15759, #15791, #15792, #15774, #15769, #15768, #15770, #15755, #15771, #15737, #15690, #15733, #15730, #15687, #15660, #15735, #15688, #15705, #15590, #15663, #15665, #15658, #15594, #15620, #15644, #15648, #15605, #15639, #15642, #15619, #15618, #15611, #15597, #15589, #15580, #15593, #15437, #15584, #15582, #15448, #15351, #15317, #15353, #15320, #15319, @B-Step62; #16151, #16142, #16111, #16106, #16051, #16046, #16044, #15971, #15957, #15810, #15749, #15706, #15683, #15728, #15732, #15707, #15621, #15567, #15566, #15523, #15479, #15404, #15400, #15378, @TomeHirata; #16026, #16072, @AveshCSingh; #15967, @euirim; #15884, #15924, #15395, #15393, #15390, @daniellok-db; #15786, @rahuja23; #15734, @lhrotk; #15809, #15739, #15695, #15654, #15694, #15655, #15653, #15608, #15543, #15573, @dhruyads; #15596, @mrharishkumar; #15742, #15723, #15633, #15606, @ShaylanDias; #15703, #15637, #15613, #15473, @joelrobin18; #15636, #15659, #15616, #15617, @raymondzhou-db; #15674, #15598, #15357, #15586, @WeichenXu123; #15691, @artjen; #15698, @prithvikannan; #15631, @hubertzub-db; #15569, @Anand1923; #15578, @y-okt; #14790, @singh-kristian; #14129, @jamblejoe; #15552, @BenWilson2; #14197, @clarachristiansen; #15505, @Conor0Callaghan; #15509, @tr33k; #15507, @vzamboulingame; #15459, @UnMelow; #13991, @abhishekpawar1060; #12161, @zhouyou9505; #15293, @tornikeo

## 2.22.1 (2025-06-06)

MLflow 2.22.1 includes several major features and improvements

Features:

- [Scoring] For DBConnect client, make spark_udf support DBR 15.4 and DBR dedicated cluster (#15938, @WeichenXu123)

Bug fixes:

- [Model Registry] Log Resources from SystemAuthPolicy in CreateModelVersion (#15485, @aravind-segu)
- [Tracking] Trace search: Avoid spawning threads for span fetching if include_spans=False (#15635, @dbczumar)

Documentation updates:

- [Docs] Spark UDF Doc update (#15586, @WeichenXu123)

Small bug fixes and documentation updates:

#15523, #15728, @TomeHirata; #13997, #16025, #15647, #16030, @harupy; #15786, @rahuja23; #15703, @joelrobin18; #15612, @serena-ruan; #16031, @daniellok-db; #15841, @frontsideair; #15807, @B-Step62

## 2.22.0 (2025-04-24)

MLflow 2.22.0 brings important bug fixes and improves the UI and tracking capabilities.

Features:

- [Tracking] Supported tracing for OpenAI Responses API.  
  (#15240, @B-Step62)
- [Tracking] Introduced `get_last_active_trace`, which affects model serving/monitoring logic.  
  (#15233, @B-Step62)
- [Tracking] Introduced async export for Databricks traces (default behavior).  
  (#15163, @B-Step62)
- [AI Gateway] Added Gemini embeddings support with corresponding unit tests.  
  (#15017, @joelrobin18)
- [Tracking / SQLAlchemy] MySQL SSL connections are now supported with client certs.  
  (#14839, @aksylumoed)
- [Models] Added Optuna storage utility for enabling parallel hyperparameter tuning.  
  (#15243, @XiaohanZhangCMU)
- [Artifacts] Added support for Azure Data Lake Storage (ADLS) artifact repositories.  
  (#14723, @serena-ruan)
- [UI] Artifact views for text now auto-refresh in the UI.  
  (#14939, @joelrobin18)

Bug Fixes:

- [Tracking / UI] Fixed serialization for structured output in `langchain_tracer` + added unit tests.  
  (#14971, @joelrobin18)
- [Server-infra] Enforced password validation for authentication (min. 8 characters).  
  (#15287, @WeichenXu123)
- [Deployments] Resolved an issue with the OpenAI Gateway adapter.  
  (#15286, @WeichenXu123)
- [Artifacts / Tracking / Server-infra] Normalized paths by stripping trailing slashes.  
  (#15016, @tarek7669)
- [Tags] Fixed bug where tag values containing `": "` were being truncated.  
  (#14896, @harupy)

Small bug fixes and documentation updates:

#15396, #15379, #15292, #15305, #15078, #15251, #15267, #15208, #15104, #15045, #15084, #15055, #15056, #15048, #14946, #14956, #14903, #14854, #14830, @serena-ruan; #15417, #15256, #15186, #15007, @TomeHirata; #15119, @bbqiu; #15413, #15314, #15311, #15303, #15301, #15288, #15275, #15269, #15272, #15268, #15262, #15266, #15264, #15261, #15252, #15249, #15244, #15236, #15235, #15237, #15140, #14982, #14898, #14893, #14861, #14870, #14853, #14849, #14813, #14822, @harupy; #15333, #15298, #15300, #15156, #15019, #14957, @B-Step62; #15313, #15297, #14880, @daniellok-db; #15066, #15074, #14913, @joelrobin18; #15232, @kbolashev; #15242, @dbczumar; #15210, #15178, @WeichenXu123; #15187, #15177, @hubertzub-db; #15059, #15070, #15050, #15012, #14959, #14918, #15005, #14965, #14858, #14930, #14927, #14786, #14883, #14863, #14852, #14788, @Gumichocopengin8; #14920, #14919, @jaceklaskowski

## 2.21.3 (2025-04-03)

MLflow 2.21.3 includes a few bugi

Bug fixes:

- [Tracking] Fix spark ML save model error in Databricks shared or serverless cluster (#15198, @WeichenXu123)
- [Tracking] Fix Spark model logging / loading in Databricks shared cluster and serverless (#15075, @WeichenXu123)

Documentation updates:

- [Docs] Add document page for DSPy optimizer tracking (#15143, @TomeHirata)

Small bug fixes and documentation updates:

#15205, @mlflow-app[bot]; #15184, #15157, #15137, @TomeHirata; #15085, @B-Step62; #15118, @bbqiu; #15172, @harupy

## 2.21.2 (2025-03-26)

MLflow 2.21.2 is a patch release that introduces minor features and bug fixes.

- Fix connection exhausting when exporting traces to Databricks (#15124, @B-Step62)
- Add logging of result table for DSPy optimizer tracking (#15061, @TomeHirata)

## 2.21.1 (2025-03-25)

MLflow 2.21.1 is a patch release that introduces minor features and addresses some minor bugs.

Features:

- [Tracking] Introduce support for logging evaluations within DSPy (#14962, @TomeHirata)
- [Tracking] Add support for run creation when DSPy compile is executed (#14949, @TomeHirata)
- [Docker / Sagemaker] Add support for building a SageMaker serving container that does not contain Java via the `--install-java option` (#14868, @rgangopadhya)

Bug fixes:

- [Tracing] Fix an issue with trace ordering due to a timestamp conversion timezone bug (#15094, @orm011)
- [Tracking] Fix a typo in the environment variable `OTEL_EXPORTER_OTLP_PROTOCOL` definition (#15008, @gabrielfu)
- [Tracking] Fix an issue in shared and serverless clusters on Databricks when logging Spark Datasources when using the evaluate API (#15077, @WeichenXu123)
- [UI] Fix a rendering issue with displaying images from within the metric tab in the UI (#15034, @TomeHirata)

Documentation updates:

- [Docs] Add additional contextual information within the set_retriever_schema API docs (#15099, @smurching)

Small bug fixes and documentation updates:

#15009, #14995, #15039, #15040, @TomeHirata; #15010, #15053, @B-Step62; #15014, #15025, #15030, #15050, #15070, @Gumichocopengin8; #15035, #15064, @joelrobin18; #15058, @serena-ruan; #14945, @turbotimon

## 2.21.0 (2025-03-14)

We are excited to announce the release of MLflow 2.21.0! This release includes a number of significant features, enhancements, and bug fixes.

### Major New Features

- 📚 **Documentation Redesign**: [MLflow documentation](https://mlflow.org/docs/latest/) is fully revamped with a new MDX-based website that provides better navigation and makes it easier to find the information you need! (#13645, @daniellok-db)
- 🤖 **Prompt Registry**: [MLflow Prompt Registry](https://mlflow.org/docs/latest/prompts/) is a powerful tool that streamlines prompt engineering and management in your GenAI applications. It enables you to version, track, and reuse prompts across your organization. (#14795, #14834, #14936, @B-Step62, #14960, #14984, @daniellok-db, #14909, @hubertzub-db)
- ⚡️ **FastAPI Scoring Server**: The [MLflow inference server](https://mlflow.org/docs/latest/deployment/deploy-model-locally/#serving-frameworks) has been migrated from Flask to FastAPI, enabling ASGI-based scalable inference for improved performance and throughput. (#14307, @TomeHirata)
- 🔍 **Enhanced Tracing Capabilities**: [MLflow Tracing](https://mlflow.org/docs/latest/tracing/) now supports synchronous/asynchronous generators and auto-tracing for Async OpenAI, providing more flexible and comprehensive tracing options. (#14459, #14400, #14793, @14792, @B-Step62)

Features:

- [Tracking] Support OpenAI Agent SDK Auto Tracing (#14987, @B-Step62)
- [Sqlalchemy / Tracking] Support mysql ssl connections with client certs (#14839, @aksylumoed)
- [Artifacts] Supports ADLS artifact repo (#14723, @serena-ruan)
- [Tracking] Add import and docs for txtai integration (#14712, @B-Step62)
- [Models] Introduce User Auth Policy for Pyfunc Models (#14538, @aravind-segu)
- [Tracking] Support new Google GenAI SDK (#14576, @TomeHirata)
- [Tracking] Support generating traces from DSPy built-in compilation and evaluation (#14400, @B-Step62)
- [Tracking] Add mlflow.log_trace API (#14418, @TomeHirata)
- [Models] ChatAgent LangGraph and LangChain Connectors (#14215, @bbqiu)

Bug fixes:

- [Models] Fix infinite recursion error with warning handler module (#14954, @BenWilson2)
- [Model Registry] Fix invalid type issue for ModelRegistry RestStore (#14980, @B-Step62)
- [Tracking] Fix: `ExperimentViewRunsControlsActionsSelectTags` doesn't set loading state to `false` when `set-tag` request fails. (#14907, @harupy)
- [Tracking] Fix a bug in tag creation where tag values containing `": "` get truncated (#14896, @harupy)
- [Tracking] Fix false alert from AMD GPU monitor (#14884, @B-Step62)
- [Tracking] Fix `mlflow.doctor` to fall back to `mlflow-skinny` when `mlflow` is not found (#14782, @harupy)
- [Models] Handle LangGraph breaking change (#14794, @B-Step62)
- [Tracking] Fix DSPy tracing in serving (#14743, @B-Step62)
- [Tracking] Add limit to the length of experiment artifact locations (#14416, @daniellok-db)
- [Build] Fix build.py to restore specific files #14444 (#14448, @arunkothari84)
- [Models] Fix false alert for ChatModel type hint (#14343, @B-Step62)
- [Model Registry] use aes256 to talk to s3 (#14354, @artjen)
- [Tracking] Fix LiteLLM autologging (#14340, @B-Step62)
- [Models] Fix ChatCompletionResponse for model serving Pydantic 1.x (#14332, @BenWilson2)

Documentation updates:

- [Tracking] Add guide about using MLflow tracing across thread (#14881, @B-Step62)
- [Docs] Add guide for tracing deepseek (#14826, @B-Step62)
- [Docs] Update llama Jupyter notebook source (#14754, @emmanuel-ferdman)
- [Docs] Replace Databricks Community Edition with Lighthouse [1] (#14642, @TomeHirata)
- [Docs] Update models from code guide and chat model guide to always recommend models from code (#14370, @smurching)
- [Artifacts] [DOC-FIX #14183] Improve documentation for 'artifact_uri' in 'download_artifacts' (#14225, @vinayakkgarg)

Small bug fixes and documentation updates:

#14994, #14992, #14990, #14979, #14964, #14969, #14944, #14948, #14957, #14958, #14942, #14940, #14935, #14929, #14805, #14876, #14833, #14748, #14744, #14666, #14668, #14664, #14667, #14580, #14475, #14439, #14397, #14363, #14361, #14377, #14378, #14337, #14324, #14339, #14259, @B-Step62; #14981, #14943, #14914, #14930, #14924, #14927, #14786, #14910, #14859, #14891, #14883, #14863, #14852, #14788, @Gumichocopengin8; #14946, #14978, #14956, #14906, #14903, #14854, #14860, #14857, #14824, #14830, #14767, #14772, #14770, #14766, #14651, #14629, #14636, #14572, #14498, #14328, #14265, @serena-ruan; #14989, #14895, #14880, #14878, #14866, #14821, #14817, #14815, #14765, #14803, #14773, #14783, #14784, #14776, #14759, #14541, #14553, #14540, #14499, #14495, #14481, #14479, #14456, #14022, #14411, #14407, #14408, #14315, #14346, #14325, #14322, #14326, #14310, #14309, #14320, #14308, @daniellok-db; #14986, #14904, #14898, #14893, #14861, #14870, #14853, #14849, #14813, #14822, #14818, #14802, #14804, #14814, #14779, #14796, #14735, #14731, #14728, #14734, #14727, #14726, #14721, #14719, #14716, #14692, #14683, #14687, #14684, #14674, #14673, #14662, #14652, #14650, #14648, #14647, #14646, #14639, #14637, #14635, #14634, #14633, #14630, #14628, #14624, #14623, #14621, #14619, #14615, #14613, #14603, #14601, #14600, #14597, #14570, #14564, #14554, #14551, #14550, #14515, #14529, #14528, #14525, #14516, #14514, #14486, #14476, #14472, #14477, #14364, #14431, #14414, #14398, #14412, #14399, #14359, #14369, #14381, #14349, #14350, #14347, #14348, #14342, #14329, #14250, #14318, #14323, #14306, #14280, #14279, #14272, #14270, #14263, #14222, @harupy; #14985, #14850, #14800, #14799, #14671, #14665, #14594, #14506, #14457, #14395, #14371, #14360, #14327, @TomeHirata; #14755, #14567, #14367, @bbqiu; #14892, @brilee; #14941, #14932, @hubertzub-db; #14913, @joelrobin18; #14756, @jiewpeng; #14701, @jaceklaskowski; #14568, #14450, @BenWilson2; #14535, @njbrake; #14507, @arunprd; #14489, @RuchitAgrawal; #14467, @seal07; #14460, @ManzoorAhmedShaikh; #14374, @wasup-yash; #14333, @singh-kristian; #14362, #14353, #14296, #13789, @dsuhinin; #14358, @apoxnen; #14335, @Fresnel-Fabian; #14178, @emmanuel-ferdman

## 2.20.4 (2025-03-13)

MLflow 2.20.4 is a tiny patch release to include a bug fix:

- [Tracking] fix: remove `log_trace` at top level module (#14873, @yxtay)

## 2.20.3 (2025-02-26)

MLflow 2.20.3 is a patch release includes several major features and improvements

Features:

- Implemented GPU metrics for AMD/HIP GPUs (#12694, @evenmn)
- Add txtai tracing integration (#14712, @B-Step62)
- Support new Google GenAI SDK (#14576, @TomeHirata)
- Support the new thinking content block in Anthropic Claude 3.7 models (#14733, @B-Step62)

Bug fixes:

- Resolve LangGraph tracing bug with `astream_event` API (#14598, @B-Step62)

Small bug fixes and documentation updates:

#14640, #14574, #14593, @serena-ruan; #14338, #14693, #14664, #14663, #14377, @B-Step62; #14680, @JulesLandrySimard; #14388, #14685, @harupy; #14704, @brilee; #14698, #14658, @bbqiu; #14660, #14659, #14632, #14616, #14594, @TomeHirata; #14535, @njbrake

## 2.20.2 (2025-02-13)

MLflow 2.20.2 is a patch release includes several bug fixes and features

Features:

- [Tracing] Support tracing sync/async generator function with @mlflow.trace (#14459, @B-Step62)
- [Tracing] Support generating traces from DSPy built-in compilation and evaluation (#14400, @B-Step62)
- [Models] ChatAgent interface enhancements and Langgraph connectors updates (#14368, #14567, @bbqiu)
- [Models] VariantType support in spark_udf (#14317, @serena-ruan)

Bug fixes:

- [Models] DSPy thread issue fix (#14471, @chenmoneygithub)

Documentation updates:

- [Docs] ChatAgent documentation updates (#14367, @bbqiu)

Small bug fixes and documentation updates:

#14410, #14569, #14440, @harupy; #14510, #14544, #14491, #14488, @bbqiu; #14518, @serena-ruan; #14517, #14500, #14461, #14478, @TomeHirata; #14512, @shaikmoeed; #14496, #14473, #14475, @B-Step62; #14467, @seal07; #14022, #14453, #14539, @daniellok-db; #14450, @BenWilson2; #14449, @SaiMadhavanG

## 2.20.1 (2025-01-30)

MLflow 2.20.1 is a patch release includes several bug fixes and features:

Features:

- Spark_udf support for the model signatures based on type hints (#14265, @serena-ruan)
- Helper connectors to use ChatAgent with LangChain and LangGraph (#14215, @bbqiu)
- Update classifier evaluator to draw RUC/Lift curves for CatBoost models by default (#14333, @singh-kristian)

Bug fixes:

- Fix Pydantic 1.x incompatibility issue (#14332, @BenWilson2)
- Apply temporary fix for LiteLLM tracing to workaround https://github.com/BerriAI/litellm/issues/8013 (#14340, @B-Step62)
- Fix false alert from type hint based model signature for ChatModel (#14343, @B-Step62)

Other small updates:

#14337, #14382, @B-Step62; #14356, @daniellok-db, #14354, @artjen, #14360, @TomuHirata,

## 2.20.0 (2025-01-23)

We are excited to announce the release of MLflow 2.20.0! This release includes a number of significant features, enhancements, and bug fixes.

### Major New Features

- **💡Type Hint-Based Model Signature**: Define your model's [signature](https://www.mlflow.org/docs/latest/model/signatures.html) in the most **Pythonic** way. MLflow now supports defining a model signature based on the type hints in your `PythonModel`'s `predict` function, and validating input data payloads against it. (#14182, #14168, #14130, #14100, #14099, @serena-ruan)

- **🧠 Bedrock / Groq Tracing Support**: [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html) now offers a one-line auto-tracing experience for **Amazon Bedrock** and **Groq** LLMs. Track LLM invocation within your model by simply adding `mlflow.bedrock.tracing` or `mlflow.groq.tracing` call to the code. (#14018, @B-Step62, #14006, @anumita0203)

- **🗒️ Inline Trace Rendering in Jupyter Notebook**: MLflow now supports rendering a trace UI **within** the notebook where you are running models. This eliminates the need to frequently switch between the notebook and browser, creating a seamless local model debugging experience. Check out [this blog post](https://mlflow.org/blog/mlflow-tracing-in-jupyter) for a quick demo! (#13955, @daniellok-db)

- **⚡️Faster Model Validation with `uv` Package Manager**: MLflow has adopted [uv](https://github.com/astral-sh/uv), a new Rust-based, super-fast Python package manager. This release adds support for the new package manager in the [mlflow.models.predict](https://www.mlflow.org/docs/latest/model/dependencies.html#validating-environment-for-prediction) API, enabling faster model environment validation. Stay tuned for more updates! (#13824, @serena-ruan)

- **🖥️ New Chat Panel in Trace UI**: THe MLflow Trace UI now shows a unified `chat` panel for LLM invocations. The update allows you to view chat messages and function calls in a rich and consistent UI across LLM providers, as well as inspect the raw input and output payloads. (#14211, @TomuHirata)

Other Features:

- Introduced `ChatAgent` base class for defining custom python agent (#13797, @bbqiu)
- Supported Tool Calling in DSPy Tracing (#14196, @B-Step62)
- Applied timeout override to within-request local scoring server for Spark UDF inference (#14202, @BenWilson2)
- Supported dictionary type for inference params (#14091, @serena-ruan)
- Make `context` parameter optional for calling `PythonModel` instance (#14059, @serena-ruan)
- Set default task for `ChatModel` (#14068, @stevenchen-db)

Bug fixes:

- [Tracking] Fix filename encoding issue in `log_image` (#14281, @TomeHirata)
- [Models] Fix the faithfulness metric for custom override parameters supplied to the callable metric implementation (#14220, @BenWilson2)
- [Artifacts] Update presigned URL list_artifacts to return an empty list instead of an exception (#14203, @arpitjasa-db)
- [Tracking] Fix rename permission model registry (#14139, @MohamedKHALILRouissi)
- [Tracking] Fix hard-dependency to langchain package in autologging (#14125, @B-Step62)
- [Tracking] Fix constraint name for MSSQL in migration 0584bdc529eb (#14146, @daniellok-db)
- [Scoring] Fix uninitialized `loaded_model` variable (#14109, @yang-chengg)
- [Model Registry] Return empty array when `DatabricksSDKModelsArtifactRepository.list_artifacts` is called on a file (#14027, @shichengzhou-db)

Documentation updates:

- [Docs] Add a quick guide for how to host MLflow on various platforms (#14289, @B-Step62)
- [Docs] Improve documentation for 'artifact_uri' in 'download_artifacts' (#14225, @vinayakkgarg)
- [Docs] Add a page for search_traces (#14033, @TomeHirata)

Small bug fixes and documentation updates:

#14294, #14252, #14233, #14205, #14217, #14172, #14188, #14167, #14166, #14163, #14162, #14161, #13971, @TomeHirata; #14299, #14280, #14279, #14278, #14272, #14270, #14268, #14269, #14263, #14258, #14222, #14248, #14128, #14112, #14111, #14093, #14096, #14095, #14090, #14089, #14085, #14078, #14074, #14070, #14053, #14060, #14035, #14014, #14002, #14000, #13997, #13996, #13995, @harupy; #14298, #14286, #14249, #14276, #14259, #14242, #14254, #14232, #14207, #14206, #14185, #14196, #14193, #14173, #14164, #14159, #14165, #14152, #14151, #14126, #14069, #13987, @B-Step62; #14295, #14265, #14271, #14262, #14235, #14239, #14234, #14228, #14227, #14229, #14218, #14216, #14213, #14208, #14204, #14198, #14187, #14181, #14177, #14176, #14156, #14169, #14099, #14086, #13983, @serena-ruan; #14155, #14067, #14140, #14132, #14072, @daniellok-db; #14178, @emmanuel-ferdman; #14247, @dbczumar; #13789, #14108, @dsuhinin; #14212, @aravind-segu; #14223, #14191, #14084, @dsmilkov; #13804, @kriscon-db; #14158, @Lodewic; #14148, #14147, #14115, #14079, #14116, @WeichenXu123; #14135, @brilee; #14133, @manos02; #14121, @LeahKorol; #14025, @nojaf; #13948, @benglewis; #13942, @justsomerandomdude264; #14003, @Ajay-Satish-01; #13982, @prithvikannan; #13638, @MaxwellSalmon

## 2.19.0 (2024-12-11)

We are excited to announce the release of MLflow 2.19.0! This release includes a number of significant features, enhancements, and bug fixes.

### Major New Features

- **ChatModel enhancements** - ChatModel now adopts `ChatCompletionRequest` and `ChatCompletionResponse` as its new schema. The `predict_stream` interface uses `ChatCompletionChunk` to deliver true streaming responses. Additionally, the `custom_inputs` and `custom_outputs` fields in ChatModel now utilize `AnyType`, enabling support for a wider variety of data types. **Note:** In a future version of MLflow, `ChatParams` (and by extension, `ChatCompletionRequest`) will have the default values for `n`, `temperature`, and `stream` removed. (#13782, #13857, @stevenchen-db)

- **Tracing improvements** - [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html) now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @B-Step62)

- **New Tracing Integrations** - [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html) now supports **CrewAI** and **Anthropic**, enabling a one-line, fully automated tracing experience. (#13903, @TomeHirata, #13851, @gabrielfu)

- **Any Type in model signature** - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @serena-ruan)

Other Features:

- [Tracking] Add `update_current_trace` API for adding tags to an active trace. (#13828, @B-Step62)
- [Deployments] Update databricks deployments to support AI gateway & additional update endpoints (#13513, @djliden)
- [Models] Support uv in mlflow.models.predict (#13824, @serena-ruan)
- [Models] Add type hints support including pydantic models (#13924, @serena-ruan)
- [Tracking] Add the `trace.search_spans()` method for searching spans within traces (#13984, @B-Step62)

Bug fixes:

- [Tracking] Allow passing in spark connect dataframes in mlflow evaluate API (#13889, @WeichenXu123)
- [Tracking] Fix `mlflow.end_run` inside a MLflow run context manager (#13888, @WeichenXu123)
- [Scoring] Fix spark_udf conditional check on remote spark-connect client or Databricks Serverless (#13827, @WeichenXu123)
- [Models] Allow changing max_workers for built-in LLM-as-a-Judge metrics (#13858, @B-Step62)
- [Models] Support saving all langchain runnables using code-based logging (#13821, @serena-ruan)
- [Model Registry] return empty array when DatabricksSDKModelsArtifactRepository.list_artifacts is called on a file (#14027, @shichengzhou-db)
- [Tracking] Stringify param values in client.log_batch() (#14015, @B-Step62)
- [Tracking] Remove deprecated squared parameter (#14028, @B-Step62)
- [Tracking] Fix request/response field in the search_traces output (#13985, @B-Step62)

Documentation updates:

- [Docs] Add Ollama and Instructor examples in tracing doc (#13937, @B-Step62)

Small bug fixes and documentation updates:

#13972, #13968, #13917, #13912, #13906, #13846, @serena-ruan; #13969, #13959, #13957, #13958, #13925, #13882, #13879, #13881, #13869, #13870, #13868, #13854, #13849, #13847, #13836, #13823, #13811, #13820, #13775, #13768, #13764, @harupy; #13960, #13914, #13862, #13892, #13916, #13918, #13915, #13878, #13891, #13863, #13859, #13850, #13844, #13835, #13818, #13762, @B-Step62; #13913, #13848, #13774, @TomeHirata; #13936, #13954, #13883, @daniellok-db; #13947, @AHB102; #13929, #13922, @Ajay-Satish-01; #13857, @stevenchen-db; #13773, @BenWilson2; #13705, @williamjamir; #13745, #13743, @WeichenXu123; #13895, @chenmoneygithub; #14023, @theBeginner86

## 2.18.0 (2024-11-18)

We are excited to announce the release of MLflow 2.18.0! This release includes a number of significant features, enhancements, and bug fixes.

### Python Version Update

Python 3.8 is now at an end-of-life point. With official support being dropped for this legacy version, **MLflow now requires Python 3.9**
as a minimum supported version.

> Note: If you are currently using MLflow's `ChatModel` interface for authoring custom GenAI applications, please ensure that you
> have read the future breaking changes section below.

### Major New Features

- **🦺 Fluent API Thread/Process Safety** - MLflow's fluent APIs for tracking and the model registry have been overhauled to add support for both thread and multi-process safety. You are now no longer forced to use the Client APIs for managing experiments, runs, and logging from within multiprocessing and threaded applications. (#13456, #13419, @WeichenXu123)

- **🧩 DSPy flavor** - MLflow now supports logging, loading, and tracing of `DSPy` models, broadening the support for advanced GenAI authoring within MLflow. Check out the [MLflow DSPy Flavor](https://mlflow.org/docs/latest/llms/dspy/index.html) documentation to get started! (#13131, #13279, #13369, #13345, @chenmoneygithub, #13543, #13800, #13807, @B-Step62, #13289, @michael-berk)

- **🖥️ Enhanced Trace UI** - [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html)'s UI has undergone
  a significant overhaul to bring usability and quality of life updates to the experience of auditing and investigating the contents of GenAI traces, from enhanced span content rendering using markdown to a standardized span component structure, (#13685, #13357, #13242, @daniellok-db)

- **🚄 New Tracing Integrations** - [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html) now supports **DSPy**, **LiteLLM**, and **Google Gemini**, enabling a one-line, fully automated tracing experience. These integrations unlock enhanced observability across a broader range of industry tools. Stay tuned for upcoming integrations and updates! (#13801, @TomeHirata, #13585, @B-Step62)

- **📊 Expanded LLM-as-a-Judge Support** - MLflow now enhances its evaluation capabilities with support for additional providers, including `Anthropic`, `Bedrock`, `Mistral`, and `TogetherAI`, alongside existing providers like `OpenAI`. Users can now also configure proxy endpoints or self-hosted LLMs that follow the provider API specs by using the new `proxy_url` and `extra_headers` options. Visit the [LLM-as-a-Judge](https://mlflow.org/docs/latest/llms/llm-evaluate/index.html#llm-as-a-judge-metrics) documentation for more details! (#13715, #13717, @B-Step62)

- **⏰ Environment Variable Detection** - As a helpful reminder for when you are deploying models, MLflow now detects and reminds users of environment variables set during model logging, ensuring they are configured for deployment. In addition to this, the `mlflow.models.predict` utility has also been updated to include these variables in serving simulations, improving pre-deployment validation. (#13584, @serena-ruan)

### Breaking Changes to ChatModel Interface

- **ChatModel Interface Updates** - As part of a broader unification effort within MLflow and services that rely on or deeply integrate
  with MLflow's GenAI features, we are working on a phased approach to making a consistent and standard interface for custom GenAI
  application development and usage. In the first phase (planned for release in the next few releases of MLflow), we are marking
  several interfaces as deprecated, as they will be changing. These changes will be:

  - **Renaming of Interfaces**:
    - `ChatRequest` → `ChatCompletionRequest` to provide disambiguation for future planned request interfaces.
    - `ChatResponse` → `ChatCompletionResponse` for the same reason as the input interface.
    - `metadata` fields within `ChatRequest` and `ChatResponse` → `custom_inputs` and `custom_outputs`, respectively.
  - **Streaming Updates**:
    - `predict_stream` will be updated to enable true streaming for custom GenAI applications. Currently, it returns a generator with synchronous outputs from predict. In a future release, it will return a generator of `ChatCompletionChunks`, enabling asynchronous streaming. While the API call structure will remain the same, the returned data payload will change significantly, aligning with LangChain's implementation.
  - **Legacy Dataclass Deprecation**:
    - Dataclasses in `mlflow.models.rag_signatures` will be deprecated, merging into unified `ChatCompletionRequest`, `ChatCompletionResponse`, and `ChatCompletionChunks`.

Other Features:

- [Evaluate] Add Huggingface BLEU metrics to MLflow Evaluate (#12799, @nebrass)
- [Models / Databricks] Add support for `spark_udf` when running on Databricks Serverless runtime, Databricks connect, and prebuilt python environments (#13276, #13496, @WeichenXu123)
- [Scoring] Add a `model_config` parameter for `pyfunc.spark_udf` for customization of batch inference payload submission (#13517, @WeichenXu123)
- [Tracing] Standardize retriever span outputs to a list of MLflow `Document`s (#13242, @daniellok-db)
- [UI] Add support for visualizing and comparing nested parameters within the MLflow UI (#13012, @jescalada)
- [UI] Add support for comparing logged artifacts within the Compare Run page in the MLflow UI (#13145, @jescalada)
- [Databricks] Add support for `resources` definitions for `Langchain` model logging (#13315, @sunishsheth2009)
- [Databricks] Add support for defining multiple retrievers within `dependencies` for Agent definitions (#13246, @sunishsheth2009)

Bug fixes:

- [Database] Cascade deletes to datasets when deleting experiments to fix a bug in MLflow's `gc` command when deleting experiments with logged datasets (#13741, @daniellok-db)
- [Models] Fix a bug with `Langchain`'s `pyfunc` predict input conversion (#13652, @serena-ruan)
- [Models] Fix signature inference for subclasses and `Optional` dataclasses that define a model's signature (#13440, @bbqiu)
- [Tracking] Fix an issue with async logging batch splitting validation rules (#13722, @WeichenXu123)
- [Tracking] Fix an issue with `LangChain`'s autologging thread-safety behavior (#13672, @B-Step62)
- [Tracking] Disable support for running spark autologging in a threadpool due to limitations in Spark (#13599, @WeichenXu123)
- [Tracking] Mark `role` and `index` as required for chat schema (#13279, @chenmoneygithub)
- [Tracing] Handle raw response in openai autolog (#13802, @harupy)
- [Tracing] Fix a bug with tracing source run behavior when running inference with multithreading on `Langchain` models (#13610, @WeichenXu123)

Documentation updates:

- [Docs] Add docstring warnings for upcoming changes to ChatModel (#13730, @stevenchen-db)
- [Docs] Add a contributor's guide for implementing tracing integrations (#13333, @B-Step62)
- [Docs] Add guidance in the use of `model_config` when logging models as code (#13631, @sunishsheth2009)
- [Docs] Add documentation for the use of custom library artifacts with the `code_paths` model logging feature (#13702, @TomeHirata)
- [Docs] Improve `SparkML` `log_model` documentation with guidance on how return probabilities from classification models (#13684, @WeichenXu123)

Small bug fixes and documentation updates:

#13775, #13768, #13764, #13744, #13699, #13742, #13703, #13669, #13682, #13569, #13563, #13562, #13539, #13537, #13533, #13408, #13295, @serena-ruan; #13768, #13764, #13761, #13738, #13737, #13735, #13734, #13723, #13726, #13662, #13692, #13689, #13688, #13680, #13674, #13666, #13661, #13625, #13460, #13626, #13546, #13621, #13623, #13603, #13617, #13614, #13606, #13600, #13583, #13601, #13602, #13604, #13598, #13596, #13597, #13531, #13594, #13589, #13581, #13112, #13587, #13582, #13579, #13578, #13545, #13572, #13571, #13564, #13559, #13565, #13558, #13541, #13560, #13556, #13534, #13386, #13532, #13385, #13384, #13383, #13507, #13523, #13518, #13492, #13493, #13487, #13490, #13488, #13449, #13471, #13417, #13445, #13430, #13448, #13443, #13429, #13418, #13412, #13382, #13402, #13381, #13364, #13356, #13309, #13313, #13334, #13331, #13273, #13322, #13319, #13308, #13302, #13268, #13298, #13296, @harupy; #13705, @williamjamir; #13632, @shichengzhou-db; #13755, #13712, #13260, @BenWilson2; #13745, #13743, #13697, #13548, #13549, #13577, #13349, #13351, #13350, #13342, #13341, @WeichenXu123; #13807, #13798, #13787, #13786, #13762, #13749, #13733, #13678, #13721, #13611, #13528, #13444, #13450, #13360, #13416, #13415, #13336, #13305, #13271, @B-Step62; #13808, #13708, @smurching; #13739, @fedorkobak; #13728, #13719, #13695, #13677, @TomeHirata; #13776, #13736, #13649, #13285, #13292, #13282, #13283, #13267, @daniellok-db; #13711, @bhavya2109sharma; #13693, #13658, @aravind-segu; #13553, @dsuhinin; #13663, @gitlijian; #13657, #13629, @parag-shendye; #13630, @JohannesJungbluth; #13613, @itepifanio; #13480, @agjendem; #13627, @ilyaresh; #13592, #13410, #13358, #13233, @nojaf; #13660, #13505, @sunishsheth2009; #13414, @lmoros-DB; #13399, @Abubakar17; #13390, @KekmaTime; #13291, @michael-berk; #12511, @jgiannuzzi; #13265, @Ahar28; #13785, @Rick-McCoy; #13676, @hyolim-e; #13718, @annzhang-db; #13705, @williamjamir

## 2.17.2 (2024-10-31)

MLflow 2.17.2 includes several major features and improvements

Features:

- [Model Registry] DatabricksSDKModelsArtifactRepository support (#13203, @shichengzhou-db)
- [Tracking] Support extracting new UCFunctionToolkit as model resources (#13567, @serena-ruan)

Bug fixes:

- [Models] Fix RunnableBinding saving (#13566, @B-Step62)
- [Models] Pin numpy when pandas < 2.1.2 in pip requirements (#13580, @serena-ruan)

Documentation updates:

- [Docs] ChatModel tool calling tutorial (#13542, @daniellok-db)

Small bug fixes and documentation updates:

#13569, @serena-ruan; #13595, @BenWilson2; #13593, @mnijhuis-dnb;

## 2.17.1 (2024-10-25)

MLflow 2.17.1 includes several major features and improvements

Features:

- [Tracking] Support custom chat endpoint without endpoint type set as llm judge (#13538, @B-Step62)
- [Tracking] Support tracing for OpenAI Swarm (#13497, @B-Step62)
- [Tracking] Support UC Connections as model dependency and resources (#13481, #13491 @sunishsheth2009)
- [Tracking] Support Genie Spaces as model resources (#13441, @aravind-segu)
- [Models] Support new Transformers task for llm/v1/embedding (#13468, @B-Step62)

Bug fixes:

- [Tracking] Fix tool span inputs/outputs format in LangChain autolog (#13527, @B-Step62)
- [Models] Fix code_path handling for LlamaIndex flavor (#13486, @B-Step62)
- [Models] Fix signature inference for subclass and optional dataclasses (#13440, @bbqiu)
- [Tracking] Fix error thrown in set_retriever_schema's behavior when it's called twice (#13422, @sunishsheth2009)
- [Tracking] Fix dependency extraction from RunnableCallables (#13423, @aravind-segu)

Documentation updates:

- [Docs] Fixed typo in docs (#13478, @JAMNESIA)
- [Docs] Improve CLI docs - attention about setting MLFLOW_TRACKING_URI (#13465, @BartoszLitwiniuk)
- [Docs] Add documentation for infer_signature usage with GenAI flavors (#13407, @serena-ruan)

Small bug fixes and documentation updates:

#13293, #13510, #13501, #13506, #13446, @harupy; #13341, #13342, @WeichenXu123; #13396, @dvorst; #13535, @chenmoneygithub; #13503, #13469, #13416, @B-Step62; #13519, #13516, @serena-ruan; #13504, @sunishsheth2009; #13508, @KamilStachera; #13397, @kriscon-db

## 2.17.0 (2024-09-26)

We are excited to announce the release of MLflow 2.17.0! This release includes several enhancements to extend the
functionality of MLflow's ChatModel interface to further extend its versatility for handling custom GenAI application use cases.
Additionally, we've improved the interface within the tracing UI to provide a structured output for retrieved documents,
enhancing the ability to read the contents of those documents within the UI.
We're also starting the work on improving both the utility and the versatility of MLflow's evaluate functionality for GenAI,
initially with support for callable GenAI evaluation metrics.

### Major Features and notifications:

- **ChatModel enhancements** - As the GenAI-focused 'cousin' of `PythonModel`, `ChatModel` is getting some sizable functionality
  extensions. From native support for tool calling (a requirement for creating a custom agent), simpler conversions to the
  internal dataclass constructs needed to interface with `ChatModel` via the introduction of `from_dict` methods to all data structures,
  the addition of a `metadata` field to allow for full input payload customization, handling of the new `refusal` response type, to the
  inclusion of the interface type to the response structure to allow for greater integration compatibility.
  (#13191, #13180, #13143, @daniellok-db, #13102, #13071, @BenWilson2)

- **Callable GenAI Evaluation Metrics** - As the initial step in a much broader expansion of the functionalities of `mlflow.evaluate` for
  GenAI use cases, we've converted the GenAI evaluation metrics to be callable. This allows you to use them directly in packages that support
  callable GenAI evaluation metrics, as well as making it simpler to debug individual responses when prototyping solutions. (#13144, @serena-ruan)

- **Audio file support in the MLflow UI** - You can now directly 'view' audio files that have been logged and listen to them from within the MLflow UI's
  artifact viewer pane.

- **MLflow AI Gateway is no longer deprecated** - We've decided to revert our deprecation for the AI Gateway feature. We had renamed it to the
  MLflow Deployments Server, but have reconsidered and reverted the naming and namespace back to the original configuration.

Features:

- [Tracing] Add Standardization to retriever span outputs within MLflow tracing (#13242, @daniellok-db)
- [Models] Add support for LlamaIndex `Workflows` objects to be serialized when calling `log_model()` (#13277, #13305, #13336, @B-Step62)
- [Models] Add tool calling support for ChatModel (#13191, @daniellok-db)
- [Models] Add `from_dict()` function to ChatModel dataclasses (#13180, @daniellok-db)
- [Models] Add metadata field for ChatModel (#13143, @daniellok-db)
- [Models] Update ChatCompletionResponse to populate object type (#13102, @BenWilson2)
- [Models] Add support for LLM response refusal (#13071, @BenWilson2)
- [Models] Add support for resources to be passed in via `langchain.log_model()` (#13315, @sunishsheth2009)
- [Tracking] Add support for setting multiple retrievers' schema via `set_retriever_schema` (#13246, @sunishsheth2009)
- [Eval] Make Evaluation metrics callable (#13144, @serena-ruan)
- [UI] Add audio support to artifact viewer UI (#13017, @sydneyw-spotify)
- [Databricks] Add support for route_optimized parameter in databricks deployment client (#13222, @prabhatkgupta)

Bug fixes:

- [Tracking] Fix tracing for LangGraph (#13215, @B-Step62)
- [Tracking] Fix an issue with `presigned_url_artifact` requests being in the wrong format (#13366, @WeichenXu123)
- [Models] Update Databricks dependency extraction functionality to work with the `langchain-databricks` partner package. (#13266, @B-Step62)
- [Model Registry] Fix retry and credential refresh issues with artifact downloads from the model registry (#12935, @rohitarun-db)
- [Tracking] Fix LangChain autologging so that langchain-community is not required for partner packages (#13172, @B-Step62)
- [Artifacts] Fix issues with file removal for the local artifact repository (#13005, @rzalawad)

Documentation updates:

- [Docs] Add guide for building custom GenAI apps with ChatModel (#13207, @BenWilson2)
- [Docs] Add updates to the MLflow AI Gateway documentation (#13217, @daniellok-db)
- [Docs] Remove MLflow AI Gateway deprecation status (#13153, @BenWilson2)
- [Docs] Add contribution guide for MLflow tracing integrations (#13333, @B-Step62)
- [Docs] Add documentation regarding the `run_id` parameter within the `search_trace` API (#13251, @B-Step62)

Small bug fixes and documentation updates:

#13372, #13271, #13243, #13226, #13190, #13230, #13208, #13130, #13045, #13094, @B-Step62; #13302, #13238, #13234, #13205, #13200, #13196, #13198, #13193, #13192, #13194, #13189, #13184, #13182, #13161, #13179, #13178, #13110, #13162, #13173, #13171, #13169, #13168, #13167, #13156, #13127, #13133, #13089, #13073, #13057, #13058, #13067, #13062, #13061, #13052, @harupy; #13295, #13219, #13038, @serena-ruan; #13176, #13164, @WeichenXu123; #13163, @gabrielfu; #13186, @varshinimuthukumar1; #13128, #13115, @nojaf; #13120, @levscaut; #13152, #13075, @BenWilson2; #13138, @tanguylefloch-veesion; #13087, @SeanAverS; #13285, #13051, #13043, @daniellok-db; #13224, @levscaut;

## 2.16.2 (2024-09-17)

MLflow 2.16.2 includes several major features and improvements

Bug fixes:

- [Models] Revert "Update Dependency Extraction for Agents (#13105)" (#13155, @aravind-segu)

## 2.16.1 (2024-09-13)

MLflow 2.16.1 is a patch release that includes some minor feature improvements and addresses several bug fixes.

Features:

- [Tracing] Add Support for an Open Telemetry compatible exporter to configure external sinks for MLflow traces (#13118, @B-Step62)
- [Model Registry, AWS] Add support for utilizing AWS KMS-based encryption for the MLflow Model Registry (#12495, @artjen)
- [Model Registry] Add support for using the OSS Unity Catalog server as a Model Registry (#13034, #13065, #13066, @rohitarun-db)
- [Models] Introduce path-based transformers logging to reduce memory requirements for saving large transformers models (#13070, @B-Step62)

Bug fixes:

- [Tracking] Fix a data payload size issue with `Model.get_tags_dict` by eliminating the return of the internally-used `config` field (#13086, @harshilprajapati96)
- [Models] Fix an issue with LangChain Agents where sub-dependencies were not being properly extracted (#13105, @aravind-segu)
- [Tracking] Fix an issue where the wrong checkpoint for the current best model in auto checkpointing was being selected (#12981, @hareeen)
- [Tracking] Fix an issue where local timezones for trace initialization were not being taken into account in AutoGen tracing (#13047, @B-Step62)

Documentation updates:

- [Docs] Added RunLLM chat widget to MLflow's documentation site (#13123, @likawind)

Small bug fixes and documentation updates:

#13140, #13141, #13098, #13091, #13101, #13100, #13095, #13044, #13048, @B-Step62; #13142, #13092, #13132, #13055, #13049, @harupy; #13135, #13036, #13029, @serena-ruan; #13134, #13081, #13078, @daniellok-db; #13107, #13103, @kriscon-db; #13104, @arpitjasa-db; #13022, @nojaf; #13069, @minihat; #12879, @faizankshaikh

## 2.16.0 (2024-08-30)

We are excited to announce the release of MLflow 2.16.0. This release includes many major features and improvements!

### Major features:

- **LlamaIndex Enhancements**🦙 - to provide additional flexibility to the [LlamaIndex integration](https://mlflow.org/docs/latest/llms/llama-index/index.html), we now have support for the [models-from-code](https://mlflow.org/docs/latest/models.html#models-from-code) functionality for logging, extended engine-based logging, and broadened support for external vector stores.

- **LangGraph Support** - We've expanded the LangChain integration to support the agent framework [LangGraph](https://langchain-ai.github.io/langgraph/). With tracing and support for logging using the models-from-code feature, creating and storing agent applications has never been easier!

- **AutoGen Tracing** - Full automatic support for tracing multi-turn agent applications built with [Microsoft's AutoGen](https://microsoft.github.io/autogen/) framework is now available in MLflow. Enabling autologging via `mlflow.autogen.autolog()` will instrument your agents built with AutoGen.

- **Plugin support for AI Gateway** - You can now define your own provider interfaces that will work with MLflow's AI Gateway (also known as the MLflow Deployments Server). Creating an installable provider definition will allow you to connect the Gateway server to any GenAI service of your choosing.

Features:

- [UI] Add updated deployment usage examples to the MLflow artifact viewer (#13024, @serena-ruan, @daniellok-db)
- [Models] Support logging LangGraph applications via the models-from-code feature (#12996, @B-Step62)
- [Models] Extend automatic authorization pass-through support for Langgraph agents (#13001, @aravind-segu)
- [Models] Expand the support for LangChain application logging to include UCFunctionToolkit dependencies (#12966, @aravind-segu)
- [Models] Support saving LlamaIndex engine directly via the models-from-code feature (#12978, @B-Step62)
- [Models] Support models-from-code within the LlamaIndex flavor (#12944, @B-Step62)
- [Models] Remove the data structure conversion of input examples to ensure enhanced compatibility with inference signatures (#12782, @serena-ruan)
- [Models] Add the ability to retrieve the underlying model object from within `pyfunc` model wrappers (#12814, @serena-ruan)
- [Models] Add spark vector UDT type support for model signatures (#12758, @WeichenXu123)
- [Tracing] Add tracing support for AutoGen (#12913, @B-Step62)
- [Tracing] Reduce the latency overhead for tracing (#12885, @B-Step62)
- [Tracing] Add Async support for the trace decorator (#12877, @MPKonst)
- [Deployments] Introduce a plugin provider system to the AI Gateway (Deployments Server) (#12611, @gabrielfu)
- [Projects] Add support for parameter submission to MLflow Projects run in Databricks (#12854, @WeichenXu123)
- [Model Registry] Introduce support for Open Source Unity Catalog as a model registry service (#12888, @artjen)

Bug fixes:

- [Tracking] Reduce the contents of the `model-history` tag to only essential fields (#12983, @harshilprajapati96)
- [Models] Fix the behavior of defining the device to utilize when loading transformers models (#12977, @serena-ruan)
- [Models] Fix evaluate behavior for LlamaIndex (#12976, @B-Step62)
- [Models] Replace `pkg_resources` with `importlib.metadata` due to package deprecation (#12853, @harupy)
- [Tracking] Fix error handling for OpenAI autolog tracing (#12841, @B-Step62)
- [Tracking] Fix a condition where a deadlock can occur when connecting to an SFTP artifact store (#12938, @WeichenXu123)
- [Tracking] Fix an issue where code_paths dependencies were not properly initialized within the system path for LangChain models (#12923, @harshilprajapati96)
- [Tracking] Fix a type error for metrics value logging (#12876, @beomsun0829)
- [Tracking] Properly catch NVML errors when collecting GPU metrics (#12903, @chenmoneygithub)
- [Deployments] Improve Gateway schema support for the OpenAI provider (#12781, @danilopeixoto)
- [Model Registry] Fix deletion of artifacts when downloading from a non-standard DBFS location during UC model registration (#12821, @smurching)

Documentation updates:

- [Docs] Add documentation guides for LangGraph support (#13025, @BenWilson2)
- [Docs] Add additional documentation for models from code feature (#12936, @BenWilson2)
- [Docs] Add documentation for model serving input payloads (#12848, @serena-ruan)

Small bug fixes and documentation updates:

#12987, #12991, #12974, #12975, #12932, #12893, #12851, #12793, @serena-ruan; #13019, #13013, @aravind-segu; #12943, @piyushdaftary; #12906, #12898, #12757, #12750, #12727, @daniellok-db; #12995, #12985, #12964, #12962, #12960, #12953, #12951, #12937, #12914, #12929, #12907, #12897, #12880, #12865, #12864, #12862, #12850, #12847, #12833, #12835, #12826, #12824, #12795, #12796, @harupy; #12592, @antbbn; #12993, #12984, #12899, #12745, @BenWilson2; #12965, @nojaf; #12968, @bbqiu; #12956, @mickvangelderen; #12939, #12950, #12915, #12931, #12919, #12889, #12849, #12794, #12779, #12836, #12823, #12737, @B-Step62; #12903, @chenmoneygithub; #12905, @Atry; #12884, #12858, #12807, #12800, #10874, @WeichenXu123; #12342, @kriscon-db; #12742, @edwardfeng-db

## 2.15.1 (2024-08-06)

MLflow 2.15.1 is a patch release that addresses several bug fixes.

Bug fixes:

- [Tracking] Fix silent disabling of LangChain autologging for LangChain >= 0.2.10. (#12779, @B-Step62)
- [Tracking] Fix `mlflow.evaluate` crash on binary classification with data subset only contains single class (#12825, @serena-ruan)
- [Tracking] Fix incompatibility of MLflow Tracing with LlamaIndex >= 0.10.61 (#12890, @B-Step62)
- [Tracking] Record exceptions in OpenAI autolog tracing (#12841, @B-Step62)
- [Tracking] Fix url with e2 proxy (#12873, @chenmoneygithub)
- [Tracking] Fix regression of connecting to MLflow tracking server on other Databricks workspace (#12861, @WeichenXu123)
- [UI] Fix refresh button for model metrics on Experiment and Run pages (#12869, @beomsun0829)

Documentation updates:

- [Docs] Update doc for Spark ML vector type (#12827, @WeichenXu123)

Small bug fixes and documentation updates:

#12823, #12860, #12844, #12843, @B-Step62; #12863, #12828, @harupy; #12845, @djliden; #12820, @annzhang-db; #12831, @chenmoneygithub

## 2.15.0 (2024-07-29)

We are excited to announce the release candidate for MLflow 2.15.0. This release includes many major features and improvements!

### Major features:

- **LlamaIndex Flavor**🦙 - MLflow now offers a native integration with [LlamaIndex](https://www.llamaindex.ai/), one of the most popular libraries for building GenAI apps centered around custom data. This integration allows you to log LlamaIndex indices within MLflow, allowing for the loading and deployment of your indexed data for inference tasks with different engine types. MLflow also provides comprehensive tracing support for LlamaIndex operations, offering unprecedented transparency into complex queries. Check out the [MLflow LlamaIndex documentation](https://mlflow.org/docs/latest/llms/llama-index/index.html) to get started! (#12633, @michael-berk, @B-Step62)

- **OpenAI Tracing**🔍 - We've enhanced our OpenAI integration with a new tracing feature that works seamlessly with MLflow OpenAI autologging. You can now enable tracing of their OpenAI API usage with a single `mlflow.openai.autolog()` call, thereby MLflow will automatically log valuable metadata such as token usage and a history of your interactions, providing deeper insights into your OpenAI-powered applications. To start exploring this new capability, please check out [the tracing documentation](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing)! (#12267, @gabrielfu)

- **Enhanced Model Deployment with New Validation Feature**✅ - To improve the reliability of model deployments, MLflow has added a new method to validate your model before deploying it to an inference endpoint. This feature helps to eliminate typical errors in input and output handling, streamlining the process of model deployment and increasing confidence in your deployed models. By catching potential issues early, you can ensure a smoother transition from development to production. (#12710, @serena-ruan)

- **Custom Metrics Definition Recording for Evaluations**📊 - We've strengthened the flexibility of defining custom metrics for model evaluation by automatically logging and versioning metrics definitions, including models used as judges and prompt templates. With this new capability, you can ensure reproducibility of evaluations across different runs and easily reuse evaluation setups for consistency, facilitating more meaningful comparisons between different models or versions. (#12487, #12509, @xq-yin)

- **Databricks SDK Integration**🔐 - MLflow's interaction with Databricks endpoints has been fully migrated to use the [Databricks SDK](https://docs.databricks.com/en/dev-tools/sdk-python.html). This change brings more robust and reliable connections between MLflow and Databricks, and access to the latest Databricks features and capabilities. We mark the legacy databricks-cli support as deprecated and will remove in the future release. (#12313, @WeichenXu123)

- **Spark VectorUDT Support**💥 - MLflow's [Model Signature](https://mlflow.org/docs/latest/model/signatures.html) framework now supports Spark Vector UDT (User Defined Type), enabling logging and deployment of models using Spark VectorUDT with robust type validation. (#12758, @WeichenXu123)

### Other Notable Changes

Features:

- [Tracking] Add `parent_id` as a parameter to the `start_run` fluent API for alternative control flows (#12721, @Flametaa)
- [Tracking] Add U2M authentication support for connecting to Databricks from MLflow (#12713, @WeichenXu123)
- [Tracking] Support deleting remote artifacts with `mlflow gc` (#12451, @M4nouel)
- [Tracing] Traces can now be deleted conveniently via UI from the Traces tab in the experiments page (#12641, @daniellok-db)
- [Models] Introduce additional parameters for the `ChatModel` interface for GenAI flavors (#12612, @WeichenXu123)
- [Models] [Transformers] Support input images encoded with b64.encodebytes (#12087, @MadhuM02)
- [Models Registry] Add support for AWS KMS encryption for the Unity Catalog model registry integration (#12495, @artjen)
- [Models] Fix MLflow Dataset hashing logic for Pandas dataframe to use `iloc` for accessing rows (#12410, @julcsii)
- [Models Registry] Support presigned urls without headers for artifact location (#12349, @artjen)
- [UI] The experiments page in the MLflow UI has an updated look, and comes with some performance optimizations for line charts (#12641, @hubertzub-db)
- [UI] Line charts can now be configured to ignore outliers in the data (#12641, @daniellok-db)
- [UI] Creating compatibility with Kubeflow Dashboard UI (#12663, @cgilviadee)
- [UI] Add a new section to the artifact page in the Tracking UI, which shows code snippet to validate model input format before deployment (#12729, @serena-ruan)

Bug fixes:

- [Tracking] Fix the model construction bug in MLflow SHAP evaluation for scikit-learn model (#12599, @serena-ruan)
- [Tracking] File store get_experiment_by_name returns all stage experiments (#12788, @serena-ruan)
- [Tracking] Fix Langchain callback injection logic for async/streaming request (#12773, @B-Step62)
- [Tracing] [OpenAI] Fix stream tracing for OpenAI to record the correct chunk structure (#12629, @BenWilson2)
- [Tracing] [LangChain] Fix LangChain tracing bug for `.batch` call due to thread unsafety (#12701, @B-Step62)
- [Tracing] [LangChain] Fix nested trace issue in LangChain tracing. (#12705, @B-Step62)
- [Tracing] Prevent intervention between MLflow Tracing and other OpenTelemetry-based libraries (#12457, @B-Step62)
- [Models] Fix `log_model` issue in MLflow >= 2.13 that causes databricks DLT py4j service crashing (#12514, @WeichenXu123)
- [Models] [Transformers] Fix batch inference issue for Transformers Whisper model (#12575, @B-Step62)
- [Models] [LangChain] Fix the empty generator issue in `predict_stream` for `AgentExecutor` and other non-Runnable chains (#12518, @B-Step62)
- [Scoring] Fix Spark UDF permission denied issue in Databricks runtime (#12774, @WeichenXu123)

Documentation updates:

- Add documentation on authentication for Databricks UC Model Registry (#12552, @WeichenXu123)
- Adding model-from-code documentation for LangChain and Pyfunc (#12325, #12336, @sunishsheth2009)
- Add FAQ entry for viewing trace exceptions (#12309, @BenWilson2)
- Add note about `fork` vs `spawn` method when using multiprocessing for parallel runs (#12337, @B-Step62)
- Add example usage of `extract_fields` for `mlflow.search_traces` (#12319, @xq-yin)
- Replace GPT-3.5-turbo with GPT-4o-mini (#12740, #12746, @Acksout)

Small bug fixes and documentation updates:

#12727, #12709, #12685, #12667, #12673, #12602, #12601, #12655, #12641, #12635, #12634, #12584, #12428, #12388, #12352, #12298, #12750, #12727, #12757, @daniellok-db; #12726, #12733, #12691, #12622, #12579, #12581, #12285, #12311, #12357, #12339, #12338, #12705, #12797, #12787, #12784, #12771, #12737, @B-Step62; #12715, @hubertzub-db; #12722, #12804, @annzhang-db; #12676, #12680, #12665, #12664, #12671, #12651, #12649, #12647, #12637, #12632, #12603, #12343, #12328, #12286, #12793, #12770, @serena-ruan; #12670, #12613, #12473, #12506, #12485, #12477, #12468, #12464, #12443, #12807, #12800, #10874, #12761, @WeichenXu123; #12690, #12678, #12686, #12545, #12621, #12598, #12583, #12582, #12510, #12580, #12570, #12571, #12559, #12538, #12537, #12519, #12515, #12507, #12508, #12502, #12499, #12497, #12447, #12467, #12426, #12448, #12430, #12420, #12385, #12371, #12359, #12284, #12345, #12316, #12287, #12303, #12291, #12795, #12786, #12796, #12792, #12791, #12778, #12777, #12755, #12751, #12753, #12749, @harupy; #12742, #12702, #12742 @edwardfeng-db; #12605, @alxhslm; #12662, @freemso; #12577, @rafyzg; #12512, @Jaishree2310; #12491, #1274, @BenWilson2; #12549, @besarthoxhaj; #12476, @jessechancy; #12541, @amanjam; #12479, #12472, #12433, #12289, @xq-yin; #12486, #12474, #11406, @jgiannuzzi; #12463, @jsuchome; #12460, @Venki1402; #12449, @yukimori; #12318, @RistoAle97; #12440, @victolee0; #12416, @Dev-98; #11771, @lababidi; #12417, @dannikay; #12663, @cgilviadee; #12410, @julcsii; #12600, @ZTZK; #12803, @hcmturner; #12747, @michael-berk; #12342, @kriscon-db; #12766, @artjen;

## 2.14.3 (2024-07-12)

MLflow 2.14.3 is a patch release that addresses bug fixes and additional documentation for released features

Features:

- [Model Registry] Add support for server-side encryption when uploading files to AWS S3 (#12495, @artjen)

Bug fixes:

- [Models] Fix stream trace logging with the OpenAI autologging implementation to record the correct chunk structure (#12629, @BenWilson2)
- [Models] Fix batch inference behavior for Whisper-based translation models to allow for multiple audio file inputs (#12575, @B-Step62)

Documentation updates:

- [Docs] Add documentation for OpenAI autologging (#12608, @BenWilson2)

Small bug fixes and documentation updates:

#12556, #12628, @B-Step62; #12582, #12560, @harupy; #12553, @nojaf

## 2.14.2 (2024-07-03)

MLflow 2.14.2 is a patch release that includes several important bug fixes and documentation enhancements.

Bug fixes:

- [Models] Fix an issue with requirements inference error handling when disabling the default warning-only behavior (#12547, @B-Step62)
- [Models] Fix dependency inference issues with Transformers models saved with the unified API `llm/v1/xxx` task definitions. (#12551, @B-Step62)
- [Models / Databricks] Fix an issue with MLlfow `log_model` introduced in MLflow 2.13.0 that causes Databricks DLT service to crash in some situations (#12514, @WeichenXu123)
- [Models] Fix an output data structure issue with the `predict_stream` implementation for LangChain AgentExecutor and other non-Runnable chains (#12518, @B-Step62)
- [Tracking] Fix an issue with the `predict_proba` inference method in the `sklearn` flavor when loading an sklearn pipeline object as `pyfunc` (#12554, @WeichenXu123)
- [Tracking] Fix an issue with the Tracing implementation where other services usage of OpenTelemetry would activate MLflow tracing and cause errors (#12457, @B-Step62)
- [Tracking / Databricks] Correct an issue when running dependency inference in Databricks that can cause duplicate dependency entries to be logged (#12493, @sunishsheth2009)

Documentation updates:

- [Docs] Add documentation and guides for the MLflow tracing schema (#12521, @BenWilson2)

Small bug fixes and documentation updates:

#12311, #12285, #12535, #12543, #12320, #12444, @B-Step62; #12310, #12340, @serena-ruan; #12409, #12432, #12471, #12497, #12499, @harupy; #12555, @nojaf; #12472, #12431, @xq-yin; #12530, #12529, #12528, #12527, #12526, #12524, #12531, #12523, #12525, #12522, @dbczumar; #12483, @jsuchome; #12465, #12441, @BenWilson2; #12450, @StarryZhang-whu

## 2.14.1 (2024-06-20)

MLflow 2.14.1 is a patch release that contains several bug fixes and documentation improvements

Bug fixes:

- [Models] Fix params and model_config handling for llm/v1/xxx Transformers model (#12401, @B-Step62)
- [UI] Fix dark mode user preference (#12386, @daniellok-db)
- [Docker] Fix docker image failing to build with `install_mlflow=False` (#12388, @daniellok-db)

Documentation updates:

- [Docs] Add link to langchain autologging page in doc (#12398, @xq-yin)
- [Docs] Add documentation for Models from Code (#12381, @BenWilson2)

Small bug fixes and documentation updates:

#12415, #12396, #12394, @harupy; #12403, #12382, @BenWilson2; #12397, @B-Step62

## 2.14.0 (2024-06-17)

MLflow 2.14.0 includes several major features and improvements that we're very excited to announce!

### Major features:

- **MLflow Tracing**: Tracing is powerful tool designed to enhance your ability to monitor, analyze, and debug GenAI applications by allowing you to inspect the intermediate outputs generated as your application handles a request. This update comes with an automatic LangChain integration to make it as easy as possible to get started, but we've also implemented high-level fluent APIs, and low-level client APIs for users who want more control over their trace instrumentation. For more information, check out the [guide in our docs](https://mlflow.org/docs/latest/llms/tracing/index.html)!
- **Unity Catalog Integration**: The MLflow Deployments server now has an integration with Unity Catalog, allowing you to leverage registered functions as tools for enhancing your chat application. For more information, check out [this guide](https://mlflow.org/docs/latest/llms/deployments/uc_integration.html)!
- **OpenAI Autologging**: Autologging support has now been added for the OpenAI model flavor. With this feature, MLflow will automatically log a model upon calling the OpenAI API. Each time a request is made, the inputs and outputs will be logged as artifacts. Check out [the guide](https://mlflow.org/docs/latest/llms/openai/guide/index.html#openai-autologging) for more information!

Other Notable Features:

- [Models] Support input images encoded with b64.encodebytes (#12087, @MadhuM02)
- [Tracking] Support async logging per X seconds (#12324, @chenmoneygithub)
- [Tracking] Provide a way to set urllib's connection number and max size (#12227, @chenmoneygithub)
- [Projects] Make MLflow project runner supporting submit spark job to databricks runtime >= 13 (#12139, @WeichenXu123)
- [UI] Add the "description" column to the runs table (#11996, @zhouyou9505)

Bug fixes:

- [Model Registry] Handle no headers presigned url (#12349, @artjen)
- [Models] Fix docstring order for ChatResponse class and make object field immutable (#12305, @xq-yin)
- [Databricks] Fix root user checking in get_databricks_nfs_temp_dir and get_databricks_local_temp_dir (#12186, @WeichenXu123)
- [Tracking] fix \_init_server process terminate hang (#12076, @zhouyou9505)
- [Scoring] Fix MLflow model container and slow test CI failure (#12042, @WeichenXu123)

Documentation updates:

- [Docs] Enhance documentation for autologging supported libraries (#12356, @xq-yin)
- [Tracking, Docs] Adding Langchain as a code example and doc string (#12325, @sunishsheth2009)
- [Tracking, Docs] Adding Pyfunc as a code example and doc string (#12336, @sunishsheth2009)
- [Docs] Add FAQ entry for viewing trace exceptions in Docs (#12309, @BenWilson2)
- [Docs] Add note about 'fork' vs 'spawn' method when using multiprocessing for parallel runs (#12337, @B-Step62)
- [Docs] Fix type error in tracing example for function wrapping (#12338, @B-Step62)
- [Docs] Add example usage of "extract_fields" for mlflow.search_traces in documentation (#12319, @xq-yin)
- [Docs] Update LangChain Autologging docs (#12306, @B-Step62)
- [Docs] Add Tracing documentation (#12191, @BenWilson2)

Small bug fixes and documentation updates:

#12359, #12308, #12350, #12284, #12345, #12316, #12287, #12303, #12291, #12288, #12265, #12170, #12248, #12263, #12249, #12251, #12239, #12241, #12240, #12235, #12242, #12172, #12215, #12228, #12216, #12164, #12225, #12203, #12181, #12198, #12195, #12192, #12146, #12171, #12163, #12166, #12124, #12106, #12113, #12112, #12074, #12077, #12058, @harupy; #12355, #12326, #12114, #12343, #12328, #12327, #12340, #12286, #12310, #12200, #12209, #12189, #12194, #12201, #12196, #12174, #12107, @serena-ruan; #12364, #12352, #12354, #12353, #12351, #12298, #12297, #12220, #12155, @daniellok-db; #12311, #12357, #12346, #12312, #12339, #12281, #12283, #12282, #12268, #12236, #12247, #12199, #12232, #12233, #12221, #12229, #12207, #12212, #12193, #12167, #12137, #12147, #12148, #12138, #12127, #12065, @B-Step62; #12289, #12253, #12330 @xq-yin; #11771, @lababidi; #12280, #12275, @BenWilson2; #12246, #12244, #12211, #12066, #12061, @WeichenXu123; #12278, @sunishsheth2009; #12136, @kriscon-db; #11911, @jessechancy; #12169, @hubertzub-db

## 2.13.2 (2024-06-06)

MLflow 2.13.2 is a patch release that includes several bug fixes and integration improvements to existing
features.

Features:

- [Tracking] Provide a way to set `urllib`'s connection number and max size (#12227, @chenmoneygithub)
- [Tracking] Support UC directory as MLflow MetaDataset (#12224, @chenmoneygithub)

Bug fixes:

- [Models] Fix inferring `mlflow[gateway]` as dependency when using `mlflow.deployment` module (#12264, @B-Step62)
- [Tracking] Flatten the model_config with `/` before logging as params (#12190, @sunishsheth2009)

Small bug fixes and documentation updates:

#12268, #12210, @B-Step62; #12214, @harupy; #12223, #12226, @annzhang-db; #12260, #12237, @prithvikannan; #12261, @BenWilson2; #12231, @serena-ruan; #12238, @sunishsheth2009

## 2.13.1 (2024-05-30)

MLflow 2.13.1 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next release.

Features:

- [MLflow] Add `mlflow[langchain]` extra that installs recommended versions of langchain with MLflow (#12182, @sunishsheth2009)
- [Tracking] Adding the ability to override the model_config in langchain flavor if loaded as pyfunc (#12085, @sunishsheth2009)
- [Model Registry] Automatically detect if Presigned URLs are required for Unity Catalog (#12177, @artjen)

Bug fixes:

- [Tracking] Use `getUserLocalTempDir` and `getUserNFSTempDir` to replace `getReplLocalTempDir` and `getReplNFSTempDir` in databricks runtime (#12105, @WeichenXu123)
- [Model] Updating chat model to take default input_example and predict to accept json during inference (#12115, @sunishsheth2009)
- [Tracking] Automatically call `load_context` when inferring signature in pyfunc (#12099, @sunishsheth2009)

Small bug fixes and documentation updates:

#12180, #12152, #12128, #12126, #12100, #12086, #12084, #12079, #12071, #12067, #12062, @serena-ruan; #12175, #12167, #12137, #12134, #12127, #12123, #12111, #12109, #12078, #12080, #12064, @B-Step62; #12142, @2maz; #12171, #12168, #12159, #12153, #12144, #12104, #12095, #12083, @harupy; #12160, @aravind-segu; #11990, @kriscon-db; #12178, #12176, #12090, #12036, @sunishsheth2009; #12162, #12110, #12088, #11937, #12075, @daniellok-db; #12133, #12131, @prithvikannan; #12132, #12035, @annzhang-db; #12121, #12120, @liangz1; #12122, #12094, @dbczumar; #12098, #12055, @mparkhe

## 2.13.0 (2024-05-20)

MLflow 2.13.0 includes several major features and improvements

With this release, we're happy to introduce several features that enhance the usability of MLflow broadly across a range of use cases.

### Major Features and Improvements:

- **Streamable Python Models**: The newly introduced `predict_stream` API for Python Models allows for custom model implementations that support the return of a generator object, permitting full customization for GenAI applications.

- **Enhanced Code Dependency Inference**: A new feature for automatically inferrring code dependencies based on detected dependencies within a model's implementation. As a supplement to the `code_paths` parameter, the introduced `infer_model_code_paths` option when logging a model will determine which additional code modules are needed in order to ensure that your models can be loaded in isolation, deployed, and reliably stored.

- **Standardization of MLflow Deployment Server**: Outputs from the Deployment Server's endpoints now conform to OpenAI's interfaces to provide a simpler integration with commonly used services.

Features:

- [Deployments] Update the MLflow Deployment Server interfaces to be OpenAI compatible (#12003, @harupy)
- [Deployments] Add `Togetherai` as a supported provider for the MLflow Deployments Server (#11557, @FotiosBistas)
- [Models] Add `predict_stream` API support for Python Models (#11791, @WeichenXu123)
- [Models] Enhance the capabilities of logging code dependencies for MLflow models (#11806, @WeichenXu123)
- [Models] Add support for RunnableBinding models in LangChain (#11980, @serena-ruan)
- [Model Registry / Databricks] Add support for renaming models registered to Unity Catalog (#11988, @artjen)
- [Model Registry / Databricks] Improve the handling of searching for invalid components from Unity Catalog registered models (#11961, @artjen)
- [Model Registry] Enhance retry logic and credential refresh to mitigate cloud provider token expiration failures when uploading or downloading artifacts (#11614, @artjen)
- [Artifacts / Databricks] Add enhanced lineage tracking for models loaded from Unity Catalog (#11305, @shichengzhou-db)
- [Tracking] Add resourcing metadata to Pyfunc models to aid in model serving environment configuration (#11832, @sunishsheth2009)
- [Tracking] Enhance LangChain signature inference for models as code (#11855, @sunishsheth2009)

Bug fixes:

- [Artifacts] Prohibit invalid configuration options for multi-part upload on AWS (#11975, @ian-ack-db)
- [Model Registry] Enforce registered model metadata equality (#12013, @artjen)
- [Models] Correct an issue with `hasattr` references in `AttrDict` usages (#11999, @BenWilson2)

Documentation updates:

- [Docs] Simplify the main documentation landing page (#12017, @BenWilson2)
- [Docs] Add documentation for the expanded code path inference feature (#11997, @BenWilson2)
- [Docs] Add documentation guidelines for the `predict_stream` API (#11976, @BenWilson2)
- [Docs] Add support for enhanced Documentation with the `JFrog` MLflow Plugin (#11426, @yonarbel)

Small bug fixes and documentation updates:

#12052, #12053, #12022, #12029, #12024, #11992, #12004, #11958, #11957, #11850, #11938, #11924, #11922, #11920, #11820, #11822, #11798, @serena-ruan; #12054, #12051, #12045, #12043, #11987, #11888, #11876, #11913, #11868, @sunishsheth2009; #12049, #12046, #12037, #11831, @dbczumar; #12047, #12038, #12020, #12021, #11970, #11968, #11967, #11965, #11963, #11941, #11956, #11953, #11934, #11921, #11454, #11836, #11826, #11793, #11790, #11776, #11765, #11763, #11746, #11748, #11740, #11735, @harupy; #12025, #12034, #12027, #11914, #11899, #11866, @BenWilson2; #12026, #11991, #11979, #11964, #11939, #11894, @daniellok-db; #11951, #11974, #11916, @annzhang-db; #12015, #11931, #11627, @jessechancy; #12014, #11917, @prithvikannan; #12012, @AveshCSingh; #12001, @yunpark93; #11984, #11983, #11977, #11977, #11949, @edwardfeng-db; #11973, @bbqiu; #11902, #11835, #11775, @B-Step62; #11845, @lababidi

## 2.12.2 (2024-05-08)

MLflow 2.12.2 is a patch release that includes several bug fixes and integration improvements to existing features. New features that are introduced in this patch release are intended to provide a foundation to further major features that will be released in the next 2 minor releases.

Features:

- [Models] Add an environment configuration flag to enable raising an exception instead of a warning for failures in model dependency inference (#11903, @BenWilson2)
- [Models] Add support for the `llm/v1/embeddings` task in the Transformers flavor to unify the input and output structures for embedding models (#11795, @B-Step62)
- [Models] Introduce model streaming return via `predict_stream()` for custom `pyfunc` models capable of returning a stream response (#11791, #11895, @WeichenXu123)
- [Evaluate] Add support for overriding the entire model evaluation judgment prompt within `mlflow.evaluate` for GenAI models (#11912, @apurva-koti)
- [Tracking] Add support for defining deployment resource metadata to configure deployment resources within `pyfunc` models (#11832, #11825, #11804, @sunishsheth2009)
- [Tracking] Add support for logging `LangChain` and custom `pyfunc` models as code (#11855, #11842, @sunishsheth2009)
- [Tracking] Modify MLflow client's behavior to read from a global asynchronous configuration state (#11778, #11780, @chenmoneygithub)
- [Tracking] Enhance system metrics data collection to include a GPU power consumption metric (#11747, @chenmoneygithub)

Bug fixes:

- [Models] Fix a validation issue when performing signature validation if `params` are specified (#11838, @WeichenXu123)
- [Databricks] Fix an issue where models cannot be loaded in the Databricks serverless runtime (#11758, @WeichenXu123)
- [Databricks] Fix an issue with the Databricks serverless runtime where scaled workers do not have authorization to read from the driver NFS mount (#11757, @WeichenXu123)
- [Databricks] Fix an issue in the Databricks serverless runtime where a model loaded via a `spark_udf` for inference fails due to a configuration issue (#11752, @WeichenXu123)
- [Server-infra] Upgrade the gunicorn dependency to version 22 to address a third-party security issue (#11742, @maitreyakv)

Documentation updates:

- [Docs] Add additional guidance on search syntax restrictions for search APIs (#11892, @BenWilson2)
- [Docs] Fix an issue with the quickstart guide where the Keras example model is defined incorrectly (#11848, @horw)
- [Docs] Provide fixes and updates to LangChain tutorials and guides (#11802, @BenWilson2)
- [Docs] Fix the model registry example within the docs for correct type formatting (#11789, @80rian)

Small bug fixes and documentation updates:

#11928, @apurva-koti; #11910, #11915, #11864, #11893, #11875, #11744, @BenWilson2; #11913, #11918, #11869, #11873, #11867, @sunishsheth2009; #11916, #11879, #11877, #11860, #11843, #11844, #11817, #11841, @annzhang-db; #11822, #11861, @serena-ruan; #11890, #11819, #11794, #11774, @B-Step62; #11880, @prithvikannan; #11833, #11818, #11954, @harupy; #11831, @dbczumar; #11812, #11816, #11800, @daniellok-db; #11788, @smurching; #11756, @IgorMilavec; #11627, @jessechancy

## 2.12.1 (2024-04-17)

MLflow 2.12.1 includes several major features and improvements

With this release, we're pleased to introduce several major new features that are focused on enhanced GenAI support, Deep Learning workflows involving images, expanded table logging functionality, and general usability enhancements within the UI and external integrations.

### Major Features and Improvements:

- **PromptFlow**: Introducing the new PromptFlow flavor, designed to enrich the GenAI landscape within MLflow. This feature simplifies the creation and management of dynamic prompts, enhancing user interaction with AI models and streamlining prompt engineering processes. (#11311, #11385 @brynn-code)

- **Enhanced Metadata Sharing for Unity Catalog**: MLflow now supports the ability to share metadata (and not model weights) within Databricks Unity Catalog. When logging a model, this functionality enables the automatic duplication of metadata into a dedicated subdirectory, distinct from the model's actual storage location, allowing for different sharing permissions and access control limits. (#11357, #11720 @WeichenXu123)

- **Code Paths Unification and Standardization**: We have unified and standardized the `code_paths` parameter across all MLflow flavors to ensure a cohesive and streamlined user experience. This change promotes consistency and reduces complexity in the model deployment lifecycle. (#11688, @BenWilson2)

- **ChatOpenAI and AzureChatOpenAI Support**: Support for the ChatOpenAI and AzureChatOpenAI interfaces has been integrated into the LangChain flavor, facilitating seamless deployment of conversational AI models. This development opens new doors for building sophisticated and responsive chat applications leveraging cutting-edge language models. (#11644, @B-Step62)

- **Custom Models in Sentence-Transformers**: The sentence-transformers flavor now supports custom models, allowing for a greater flexibility in deploying tailored NLP solutions. (#11635, @B-Step62)

- **Image Support for Log Table**: With the addition of image support in `log_table`, MLflow enhances its capabilities in handling rich media. This functionality allows for direct logging and visualization of images within the platform, improving the interpretability and analysis of visual data. (#11535, @jessechancy)

- **Streaming Support for LangChain**: The newly introduced `predict_stream` API for LangChain models supports streaming outputs, enabling real-time output for chain invocation via pyfunc. This feature is pivotal for applications requiring continuous data processing and instant feedback. (#11490, #11580 @WeichenXu123)

### Security Fixes:

- **Security Patch**: Addressed a critical Local File Read/Path Traversal vulnerability within the Model Registry, ensuring robust protection against unauthorized access and securing user data integrity. (#11376, @WeichenXu123)

Features:

- [Models] Add the PromptFlow flavor (#11311, #11385 @brynn-code)
- [Models] Add a new `predict_stream` API for streamable output for Langchain models and the `DatabricksDeploymentClient` (#11490, #11580 @WeichenXu123)
- [Models] Deprecate and add `code_paths` alias for `code_path` in `pyfunc` to be standardized to other flavor implementations (#11688, @BenWilson2)
- [Models] Add support for custom models within the `sentence-transformers` flavor (#11635, @B-Step62)
- [Models] Enable Spark `MapType` support within model signatures when used with Spark udf inference (#11265, @WeichenXu123)
- [Models] Add support for metadata-only sharing within Unity Catalog through the use of a subdirectory (#11357, #11720 @WeichenXu123)
- [Models] Add Support for the `ChatOpenAI` and `AzureChatOpenAI` LLM interfaces within the LangChain flavor (#11644, @B-Step62)
- [Artifacts] Add support for utilizing presigned URLs when uploading and downloading files when using Unity Catalog (#11534, @artjen)
- [Artifacts] Add a new `Image` object for handling the logging and optimized compression of images (#11404, @jessechancy)
- [Artifacts] Add time and step-based metadata to the logging of images (#11243, @jessechancy)
- [Artifacts] Add the ability to log a dataset to Unity Catalog by means of `UCVolumeDatasetSource` (#11301, @chenmoneygithub)
- [Tracking] Remove the restrictions for logging a table in Delta format to no longer require running within a Databricks environment (#11521, @chenmoneygithub)
- [Tracking] Add support for logging `mlflow.Image` files within tables (#11535, @jessechancy)
- [Server-infra] Introduce override configurations for controlling how http retries are handled (#11590, @BenWilson2)
- [Deployments] Implement `chat` & `chat streaming` for Anthropic within the MLflow deployments server (#11195, @gabrielfu)

Security fixes:

- [Model Registry] Fix Local File Read/Path Traversal (LFI) bypass vulnerability (#11376, @WeichenXu123)

Bug fixes:

- [Model Registry] Fix a registry configuration error that occurs within Databricks serverless clusters (#11719, @WeichenXu123)
- [Model Registry] Delete registered model permissions when deleting the underlying models (#11601, @B-Step62)
- [Model Registry] Disallow `%` in model names to prevent URL mangling within the UI (#11474, @daniellok-db)
- [Models] Fix an issue where critically important environment configurations were not being captured as langchain dependencies during model logging (#11679, @serena-ruan)
- [Models] Patch the `LangChain` loading functions to handle uncorrectable pickle-related exceptions that are thrown when loading a model in certain versions (#11582, @B-Step62)
- [Models] Fix a regression in the `sklearn` flavor to reintroduce support for custom prediction methods (#11577, @B-Step62)
- [Models] Fix an inconsistent and unreliable implementation for batch support within the `langchain` flavor (#11485, @WeichenXu123)
- [Models] Fix loading remote-code-dependent `transformers` models that contain custom code (#11412, @daniellok-db)
- [Models] Remove the legacy conversion logic within the `transformers` flavor that generates an inconsistent input example display within the MLflow UI (#11508, @B-Step62)
- [Models] Fix an issue with Keras autologging iteration input handling (#11394, @WeichenXu123)
- [Models] Fix an issue with `keras` autologging training dataset generator (#11383, @WeichenXu123)
- [Tracking] Fix an issue where a module would be imported multiple times when logging a langchain model (#11553, @sunishsheth2009)
- [Tracking] Fix the sampling logic within the `GetSampledHistoryBulkInterval` API to produce more consistent results when displayed within the UI (#11475, @daniellok-db)
- [Tracking] Fix import issues and properly resolve dependencies of `langchain` and `lanchain_community` within `langchain` models when logging (#11450, @sunishsheth2009)
- [Tracking] Improve the performance of asynchronous logging (#11346, @chenmoneygithub)
- [Deployments] Add middle-of-name truncation to excessively long deployment names for Sagemaker image deployment (#11523, @BenWilson2)

Documentation updates:

- [Docs] Add clarity and consistent documentation for `code_paths` docstrings in API documentation (#11675, @BenWilson2)
- [Docs] Add documentation guidance for `sentence-transformers` `OpenAI`-compatible API interfaces (#11373, @es94129)

Small bug fixes and documentation updates:

#11723, @freemin7; #11722, #11721, #11690, #11717, #11685, #11689, #11607, #11581, #11516, #11511, #11358, @serena-ruan; #11718, #11673, #11676, #11680, #11671, #11662, #11659, #11654, #11633, #11628, #11620, #11610, #11605, #11604, #11600, #11603, #11598, #11572, #11576, #11555, #11563, #11539, #11532, #11528, #11525, #11514, #11513, #11509, #11457, #11501, #11500, #11459, #11446, #11443, #11442, #11433, #11430, #11420, #11419, #11416, #11418, #11417, #11415, #11408, #11325, #11327, #11313, @harupy; #11707, #11527, #11663, #11529, #11517, #11510, #11489, #11455, #11427, #11389, #11378, #11326, @B-Step62; #11715, #11714, #11665, #11626, #11619, #11437, #11429, @BenWilson2; #11699, #11692, @annzhang-db; #11693, #11533, #11396, #11392, #11386, #11380, #11381, #11343, @WeichenXu123; #11696, #11687, #11683, @chilir; #11387, #11625, #11574, #11441, #11432, #11428, #11355, #11354, #11351, #11349, #11339, #11338, #11307, @daniellok-db; #11653, #11369, #11270, @chenmoneygithub; #11666, #11588, @jessechancy; #11661, @jmjeon94; #11640, @tunjan; #11639, @minkj1992; #11589, @tlm365; #11566, #11410, @brynn-code; #11570, @lababidi; #11542, #11375, #11345, @edwardfeng-db; #11463, @taranarmo; #11506, @ernestwong-db; #11502, @fzyzcjy; #11470, @clemenskol; #11452, @jkfran; #11413, @GuyAglionby; #11438, @victorsun123; #11350, @liangz1; #11370, @sunishsheth2009; #11379, #11304, @zhouyou9505; #11321, #11323, #11322, @michael-berk; #11333, @cdancette; #11228, @TomeHirata

## 2.12.0 (2024-04-16)

MLflow 2.12.0 has been yanked from PyPI due to an issue with packaging required JS components. MLflow 2.12.1 is its replacement.

## 2.11.3 (2024-03-21)

MLflow 2.11.3 is a patch release that addresses a security exploit with the Open Source MLflow tracking server and miscellaneous Databricks integration fixes

Bug fixes:

- [Security] Address an LFI exploit related to misuse of url parameters (#11473, @daniellok-db)
- [Databricks] Fix an issue with Databricks Runtime version acquisition when deploying a model using Databricks Docker Container Services (#11483, @WeichenXu123)
- [Databricks] Correct an issue with credential management within Databricks Model Serving (#11468, @victorsun123)
- [Models] Fix an issue with chat request validation for LangChain flavor (#11478, @BenWilson2)
- [Models] Fixes for LangChain models that are logged as code (#11494, #11436 @sunishsheth2009)

## 2.11.2 (2024-03-19)

MLflow 2.11.2 is a patch release that introduces corrections for the support of custom transformer models, resolves LangChain integration problems, and includes several fixes to enhance stability.

Bug fixes:

- [Security] Address LFI exploit (#11376, @WeichenXu123)
- [Models] Fix transformers models implementation to allow for custom model and component definitions to be loaded properly (#11412, #11428 @daniellok-db)
- [Models] Fix the LangChain flavor implementation to support defining an MLflow model as code (#11370, @sunishsheth2009)
- [Models] Fix LangChain VectorSearch parsing errors (#11438, @victorsun123)
- [Models] Fix LangChain import issue with the community package (#11450, @sunishsheth2009)
- [Models] Fix serialization errors with RunnableAssign in the LangChain flavor (#11358, @serena-ruan)
- [Models] Address import issues with LangChain community for Databricks models (#11350, @liangz1)
- [Registry] Fix model metadata sharing within Databricks Unity Catalog (#11357, #11392 @WeichenXu123)

Small bug fixes and documentation updates:

#11321, #11323, @michael-berk; #11326, #11455, @B-Step62; #11333, @cdancette; #11373, @es94129; #11429, @BenWilson2; #11413, @GuyAglionby; #11338, #11339, #11355, #11432, #11441, @daniellok-db; #11380, #11381, #11383, #11394, @WeichenXu123; #11446, @harupy;

## 2.11.1 (2024-03-06)

MLflow 2.11.1 is a patch release, containing fixes for some Databricks integrations and other various issues.

Bug fixes:

- [UI] Add git commit hash back to the run page UI (#11324, @daniellok-db)
- [Databricks Integration] Explicitly import vectorstores and embeddings in databricks_dependencies (#11334, @daniellok-db)
- [Databricks Integration] Modify DBR version parsing logic (#11328, @daniellok-db)

Small bug fixes and documentation updates:

#11336, #11335, @harupy; #11303, @B-Step62; #11319, @BenWilson2; #11306, @daniellok-db

## 2.11.0 (2024-03-01)

MLflow 2.11.0 includes several major features and improvements

With the MLflow 2.11.0 release, we're excited to bring a series of large and impactful features that span both GenAI and Deep Learning use cases.

- The MLflow Tracking UI got an overhaul to better support the review and comparison of training runs for Deep Learning workloads. From grouping to large-scale metric plotting throughout
  the iterations of a DL model's training cycle, there are a large number of quality of life improvements to enhance your Deep Learning MLOps workflow.

- Support for the popular [PEFT](https://www.mlflow.org/docs/latest/llms/transformers/guide/index.html#peft-models-in-mlflow-transformers-flavor) library from HuggingFace is now available
  in the `mlflow.transformers` flavor. In addition to PEFT support, we've removed the restrictions on Pipeline types
  that can be logged to MLflow, as well as the ability to, when developing and testing models, log a transformers pipeline without copying foundational model weights. These
  enhancements strive to make the transformers flavor more useful for cutting-edge GenAI models, new pipeline types, and to simplify the development process of prompt engineering, fine-tuning,
  and to make iterative development faster and cheaper. Give the updated flavor a try today! (#11240, @B-Step62)

- We've added support to both [PyTorch](https://www.mlflow.org/docs/latest/python_api/mlflow.pytorch.html#mlflow.pytorch.autolog) and
  [TensorFlow](https://www.mlflow.org/docs/latest/python_api/mlflow.tensorflow.html#mlflow.tensorflow.autolog) for automatic model weights checkpointing (including resumption from a
  previous state) for the auto logging implementations within both flavors. This highly requested feature allows you to automatically configure long-running Deep Learning training
  runs to keep a safe storage of your best epoch, eliminating the risk of a failure late in training from losing the state of the model optimization. (#11197, #10935, @WeichenXu123)

- We've added a new interface to Pyfunc for GenAI workloads. The new `ChatModel` interface allows for interacting with a deployed GenAI chat model as you would with any other provider.
  The simplified interface (no longer requiring conformance to a Pandas DataFrame input type) strives to unify the API interface experience. (#10820, @daniellok-db)

- We now support Keras 3. This large overhaul of the Keras library introduced new fundamental changes to how Keras integrates with different DL frameworks, bringing with it
  a host of new functionality and interoperability. To learn more, see the [Keras 3.0 Tutorial](https://www.mlflow.org/docs/latest/deep-learning/keras/quickstart/quickstart_keras.html)
  to start using the updated model flavor today! (#10830, @chenmoneygithub)

- [Mistral AI](https://mistral.ai/) has been added as a native [provider](https://www.mlflow.org/docs/latest/llms/deployments/index.html#providers) for the MLflow Deployments Server. You can
  now create proxied connections to the Mistral AI services for completions and embeddings with their powerful GenAI models. (#11020, @thnguyendn)

- We've added compatibility support for the OpenAI 1.x SDK. Whether you're using an OpenAI LLM for model evaluation or calling OpenAI within a LangChain model, you'll now be able to
  utilize the 1.x family of the OpenAI SDK without having to point to deprecated legacy APIs. (#11123, @harupy)

Features:

- [UI] Revamp the MLflow Tracking UI for Deep Learning workflows, offering a more intuitive and efficient user experience (#11233, @daniellok-db)
- [Data] Introduce the ability to log datasets without loading them into memory, optimizing resource usage and processing time (#11172, @chenmoneygithub)
- [Models] Introduce logging frequency controls for TensorFlow, aligning it with Keras for consistent performance monitoring (#11094, @chenmoneygithub)
- [Models] Add PySpark DataFrame support in `mlflow.pyfunc.predict`, enhancing data compatibility and analysis options for batch inference (#10939, @ernestwong-db)
- [Models] Introduce new CLI commands for updating model requirements, facilitating easier maintenance, validation and updating of models without having to re-log (#11061, @daniellok-db)
- [Models] Update Embedding API for sentence transformers to ensure compatibility with OpenAI format, broadening model application scopes (#11019, @lu-wang-dl)
- [Models] Improve input and signature support for text-generation models, optimizing for Chat and Completions tasks (#11027, @es94129)
- [Models] Enable chat and completions task outputs in the text-generation pipeline, expanding interactive capabilities (#10872, @es94129)
- [Tracking] Add node id to system metrics for enhanced logging in multi-node setups, improving diagnostics and monitoring (#11021, @chenmoneygithub)
- [Tracking] Implement `mlflow.config.enable_async_logging` for asynchronous logging, improving log handling and system performance (#11138, @chenmoneygithub)
- [Evaluate] Enhance model evaluation with endpoint URL support, streamlining performance assessments and integrations (#11262, @B-Step62)
- [Deployments] Implement chat & chat streaming support for Cohere, enhancing interactive model deployment capabilities (#10976, @gabrielfu)
- [Deployments] Enable Cohere streaming support, allowing real-time interaction functionalities for the MLflow Deployments server with the Cohere provider (#10856, @gabrielfu)
- [Docker / Scoring] Optimize Docker images for model serving, ensuring more efficient deployment and scalability (#10954, @B-Step62)
- [Scoring] Support completions (`prompt`) and embeddings (`input`) format inputs in the scoring server, increasing model interaction flexibility (#10958, @es94129)

Bug Fixes:

- [Model Registry] Correct the oversight of not utilizing the default credential file in model registry setups (#11261, @B-Step62)
- [Model Registry] Address the visibility issue of aliases in the model versions table within the registered model detail page (#11223, @smurching)
- [Models] Ensure `load_context()` is called when enforcing `ChatModel` outputs so that all required external references are included in the model object instance (#11150, @daniellok-db)
- [Models] Rectify the keras output dtype in signature mismatches, ensuring data consistency and accuracy (#11230, @chenmoneygithub)
- [Models] Resolve spark model loading failures, enhancing model reliability and accessibility (#11227, @WeichenXu123)
- [Models] Eliminate false warnings for missing signatures in Databricks, improving the user experience and model validation processes (#11181, @B-Step62)
- [Models] Implement a timeout for signature/requirement inference during Transformer model logging, optimizing the logging process and avoiding delays (#11037, @B-Step62)
- [Models] Address the missing dtype issue for transformer pipelines, ensuring data integrity and model accuracy (#10979, @B-Step62)
- [Models] Correct non-idempotent predictions due to in-place updates to model-config, stabilizing model outputs (#11014, @B-Step62)
- [Models] Fix an issue where specifying `torch.dtype` as a string was not being applied correctly to the underlying transformers model (#11297, #11295, @harupy)
- [Tracking] Fix `mlflow.evaluate` `col_mapping` bug for non-LLM/custom metrics, ensuring accurate evaluation and metric calculation (#11156, @sunishsheth2009)
- [Tracking] Resolve the `TensorInfo` TypeError exception message issue, ensuring clarity and accuracy in error reporting for users (#10953, @leecs0503)
- [Tracking] Enhance `RestException` objects to be picklable, improving their usability in distributed computing scenarios where serialization is essential (#10936, @WeichenXu123)
- [Tracking] Address the handling of unrecognized response error codes, ensuring robust error processing and improved user feedback in edge cases (#10918, @chenmoneygithub)
- [Spark] Update hardcoded `io.delta:delta-spark_2.12:3.0.0` dependency to the correct scala version, aligning dependencies with project requirements (#11149, @WeichenXu123)
- [Server-infra] Adapt to newer versions of python by avoiding `importlib.metadata.entry_points().get`, enhancing compatibility and stability (#10752, @raphaelauv)
- [Server-infra / Tracking] Introduce an environment variable to disable mlflow configuring logging on import, improving configurability and user control (#11137, @jmahlik)
- [Auth] Enhance auth validation for `mlflow.login()`, streamlining the authentication process and improving security (#11039, @chenmoneygithub)

Documentation Updates:

- [Docs] Introduce a comprehensive notebook demonstrating the use of ChatModel with Transformers and Pyfunc, providing users with practical insights and guidelines for leveraging these models (#11239, @daniellok-db)
- [Tracking / Docs] Stabilize the dataset logging APIs, removing the experimental status (#11229, @dbczumar)
- [Docs] Revise and update the documentation on authentication database configuration, offering clearer instructions and better support for setting up secure authentication mechanisms (#11176, @gabrielfu)
- [Docs] Publish a new guide and tutorial for MLflow data logging and `log_input`, enriching the documentation with actionable advice and examples for effective data handling (#10956, @BenWilson2)
- [Docs] Upgrade the documentation visuals by replacing low-resolution and poorly dithered GIFs with high-quality HTML5 videos, significantly enhancing the learning experience (#11051, @BenWilson2)
- [Docs / Examples] Correct the compatibility matrix for OpenAI in MLflow Deployments Server documentation, providing users with accurate integration details and supporting smoother deployments (#11015, @BenWilson2)

Small bug fixes and documentation updates:

#11284, #11096, #11285, #11245, #11254, #11252, #11250, #11249, #11234, #11248, #11242, #11244, #11236, #11208, #11220, #11222, #11221, #11219, #11218, #11210, #11209, #11207, #11196, #11194, #11177, #11205, #11183, #11192, #11179, #11178, #11175, #11174, #11166, #11162, #11151, #11168, #11167, #11153, #11158, #11143, #11141, #11119, #11123, #11124, #11117, #11121, #11078, #11097, #11079, #11095, #11082, #11071, #11076, #11070, #11072, #11073, #11069, #11058, #11034, #11046, #10951, #11055, #11045, #11035, #11044, #11043, #11031, #11030, #11023, #10932, #10986, #10949, #10943, #10928, #10929, #10925, #10924, #10911, @harupy; #11289, @BenWilson2; #11290, #11145, #11125, #11098, #11053, #11006, #11001, #11011, #11007, #10985, #10944, #11231, @daniellok-db; #11276, #11280, #11275, #11263, #11247, #11257, #11258, #11256, #11224, #11211, #11182, #11059, #11056, #11048, #11008, #10923, @serena-ruan; #11129, #11086, @victorsun123; #11292, #11004, #11204, #11148, #11165, #11146, #11115, #11099, #11092, #11029, #10983, @B-Step62; #11189, #11191, #11022, #11160, #11110, #11088, #11042, #10879, #10832, #10831, #10888, #10908, @michael-berk; #10627, #11217, #11200, #10969, @liangz1; #11215, #11173, #11000, #10931, @edwardfeng-db; #11188, #10711, @TomeHirata; #11186, @xhochy; #10916, @annzhang-db; #11131, #11010, #11060, @WeichenXu123; #11063, #10981, #10889, ##11269, @chenmoneygithub; #11054, #10921, @smurching; #11018, @mingyangge-db; #10989, @minkj1992; #10796, @kriscon-db; #10984, @eltociear; #10982, @holzman; #10972, @bmuskalla; #10959, @prithvikannan; #10941, @mahesh-venkatachalam; #10915, @Cokral; #10904, @dannyfriar; #11134, @WP-LKL; #11287, @serkef;

## 2.10.2 (2024-02-09)

MLflow 2.10.2 includes several major features and improvements

Small bug fixes and documentation updates:

#11065, @WeichenXu123

## 2.10.1 (2024-02-06)

MLflow 2.10.1 is a patch release, containing fixes for various bugs in the `transformers` and `langchain` flavors, the MLflow UI, and the S3 artifact store. More details can be found in the patch notes below.

Bug fixes:

- [UI] Fixed a bug that prevented datasets from showing up in the MLflow UI (#10992, @daniellok-db)
- [Artifact Store] Fixed directory bucket region name retrieval (#10967, @kriscon-db)
- Bug fixes for Transformers flavor
  - [Models] Fix an issue with transformer pipelines not inheriting the torch dtype specified on the model, causing pipeline inference to consume more resources than expected. (#10979, @B-Step62)
  - [Models] Fix non-idempotent prediction due to in-place update to model-config (#11014, @B-Step62)
  - [Models] Fixed a bug affecting prompt templating with Text2TextGeneration pipelines. Previously, calling `predict()` on a pyfunc-loaded Text2TextGeneration pipeline would fail for `string` and `List[string]` inputs. (#10960, @B-Step62)
- Bug fixes for Langchain flavor
  - Fixed errors that occur when logging inputs and outputs with different lengths (#10952, @serena-ruan)

Documentation updates:

- [Docs] Add indications of DL UI capabilities to the DL landing page (#10991, @BenWilson2)
- [Docs] Fix incorrect logo on LLMs landing page (#11017, @BenWilson2)

Small bug fixes and documentation updates:

#10930, #11005, @serena-ruan; #10927, @harupy

## 2.10.0 (2024-01-26)

MLflow 2.10.0 includes several major features and improvements

In MLflow 2.10, we're introducing a number of significant new features that are preparing the way for current and future enhanced support for Deep Learning use cases, new features to support a broadened support for GenAI applications, and some quality of life improvements for the MLflow Deployments Server (formerly the AI Gateway).

Our biggest features this release are:

- We have a new [home](https://mlflow.org). The new site landing page is fresh, modern, and contains more content than ever. We're adding new content and blogs all of the time.

- Objects and Arrays are now available as configurable input and output schema elements. These new types are particularly useful for GenAI-focused flavors that can have complex input and output types. See the new [Signature and Input Example documentation](https://mlflow.org/docs/latest/model/signatures.html) to learn more about how to use these new signature types.

- LangChain has autologging support now! When you invoke a chain, with autologging enabled, we will automatically log most chain implementations, recording and storing your configured LLM application for you. See the new [Langchain documentation](https://mlflow.org/docs/latest/llms/langchain/index.html#mlflow-langchain-autologging) to learn more about how to use this feature.

- The MLflow `transformers` flavor now supports prompt templates. You can now specify an application-specific set of instructions to submit to your GenAI pipeline in order to simplify, streamline, and integrate sets of system prompts to be supplied with each input request. Check out the updated [guide to transformers](https://www.mlflow.org/docs/latest/llms/transformers/index.html) to learn more and see examples!

- The [MLflow Deployments Server](https://mlflow.org/docs/latest/llms/deployments/index.html) now supports two new requested features: (1) OpenAI endpoints that support streaming responses. You can now configure an endpoint to return realtime responses for Chat and Completions instead of waiting for the entire text contents to be completed. (2) Rate limits can now be set per endpoint in order to help control cost overrun when using SaaS models.

- Continued the push for enhanced documentation, guides, tutorials, and examples by expanding on core MLflow functionality ([Deployments](https://mlflow.org/docs/latest/deployment/index.html), [Signatures](https://mlflow.org/docs/latest/model/signatures.html), and [Model Dependency management](https://mlflow.org/docs/latest/model/dependencies.html)), as well as entirely new pages for GenAI flavors. Check them out today!

Features:

- [Models] Introduce `Objects` and `Arrays` support for model signatures (#9936, @serena-ruan)
- [Models] Support saving prompt templates for transformers (#10791, @daniellok-db)
- [Models] Enhance the MLflow Models `predict` API to serve as a pre-logging validator of environment compatibility. (#10759, @B-Step62)
- [Models] Add support for Image Classification pipelines within the transformers flavor (#10538, @KonakanchiSwathi)
- [Models] Add support for retrieving and storing license files for transformers models (#10871, @BenWilson2)
- [Models] Add support for model serialization in the Visual NLP format for JohnSnowLabs flavor (#10603, @C-K-Loan)
- [Models] Automatically convert OpenAI input messages to LangChain chat messages for `pyfunc` predict (#10758, @dbczumar)
- [Tracking] Add support for Langchain autologging (#10801, @serena-ruan)
- [Tracking] Enhance async logging functionality by ensuring flush is called on `Futures` objects (#10715, @chenmoneygithub)
- [Tracking] Add support for a non-interactive mode for the `login()` API (#10623, @henxing)
- [Scoring] Allow MLflow model serving to support direct `dict` inputs with the `messages` key (#10742, @daniellok-db, @B-Step62)
- [Deployments] Add streaming support to the MLflow Deployments Server for OpenAI streaming return compatible routes (#10765, @gabrielfu)
- [Deployments] Add the ability to set rate limits on configured endpoints within the MLflow deployments server API (#10779, @TomeHirata)
- [Deployments] Add support for directly interfacing with OpenAI via the MLflow Deployments server (#10473, @prithvikannan)
- [UI] Introduce a number of new features for the MLflow UI (#10864, @daniellok-db)
- [Server-infra] Add an environment variable that can disallow HTTP redirects (#10655, @daniellok-db)
- [Artifacts] Add support for Multipart Upload for Azure Blob Storage (#10531, @gabrielfu)

Bug fixes:

- [Models] Add deduplication logic for pip requirements and extras handling for MLflow models (#10778, @BenWilson2)
- [Models] Add support for paddle 2.6.0 release (#10757, @WeichenXu123)
- [Tracking] Fix an issue with an incorrect retry default timeout for urllib3 1.x (#10839, @BenWilson2)
- [Recipes] Fix an issue with MLflow Recipes card display format (#10893, @WeichenXu123)
- [Java] Fix an issue with metadata collection when using Streaming Sources on certain versions of Spark where Delta is the source (#10729, @daniellok-db)
- [Scoring] Fix an issue where SageMaker tags were not propagating correctly (#9310, @clarkh-ncino)
- [Windows / Databricks] Fix an issue with executing Databricks run commands from within a Window environment (#10811, @wolpl)
- [Models / Databricks] Disable `mlflowdbfs` mounts for JohnSnowLabs flavor due to flakiness (#9872, @C-K-Loan)

Documentation updates:

- [Docs] Fixed the `KeyError: 'loss'` bug for the Quickstart guideline (#10886, @yanmxa)
- [Docs] Relocate and supplement Model Signature and Input Example docs (#10838, @BenWilson2)
- [Docs] Add the HuggingFace Model Evaluation Notebook to the website (#10789, @BenWilson2)
- [Docs] Rewrite the search run documentation (#10863, @chenmoneygithub)
- [Docs] Create documentation for transformers prompt templates (#10836, @daniellok-db)
- [Docs] Refactoring of the Getting Started page (#10798, @BenWilson2)
- [Docs] Add a guide for model dependency management (#10807, @B-Step62)
- [Docs] Add tutorials and guides for LangChain (#10770, @BenWilson2)
- [Docs] Refactor portions of the Deep Learning documentation landing page (#10736, @chenmoneygithub)
- [Docs] Refactor and overhaul the Deployment documentation and add new tutorials (#10726, @B-Step62)
- [Docs] Add a PyTorch landing page, quick start, and guide (#10687, #10737 @chenmoneygithub)
- [Docs] Add additional tutorials to OpenAI flavor docs (#10700, @BenWilson2)
- [Docs] Enhance the guides on quickly getting started with MLflow by demonstrating how to use Databricks Community Edition (#10663, @BenWilson2)
- [Docs] Create the OpenAI Flavor landing page and intro notebooks (#10622, @BenWilson2)
- [Docs] Refactor the Tensorflow flavor API docs (#10662, @chenmoneygithub)

Small bug fixes and documentation updates:

#10538, #10901, #10903, #10876, #10833, #10859, #10867, #10843, #10857, #10834, #10814, #10805, #10764, #10771, #10733, #10724, #10703, #10710, #10696, #10691, #10692, @B-Step62; #10882, #10854, #10395, #10725, #10695, #10712, #10707, #10667, #10665, #10654, #10638, #10628, @harupy; #10881, #10875, #10835, #10845, #10844, #10651, #10806, #10786, #10785, #10781, #10741, #10772, #10727, @serena-ruan; #10873, #10755, #10750, #10749, #10619, @WeichenXu123; #10877, @amueller; #10852, @QuentinAmbard; #10822, #10858, @gabrielfu; #10862, @jerrylian-db; #10840, @ernestwong-db; #10841, #10795, #10792, #10774, #10776, #10672, @BenWilson2; #10827, #10826, #10825, #10732, #10481, @michael-berk; #10828, #10680, #10629, @daniellok-db; #10799, #10800, #10578, #10782, #10783, #10723, #10464, @annzhang-db; #10803, #10731, #10708, @kriscon-db; #10797, @dbczumar; #10756, #10751, @Ankit8848; #10784, @AveshCSingh; #10769, #10763, #10717, @chenmoneygithub; #10698, @rmalani-db; #10767, @liangz1; #10682, @cdreetz; #10659, @prithvikannan; #10639, #10609, @TomeHirata

## 2.9.2 (2023-12-14)

MLflow 2.9.2 is a patch release, containing several critical security fixes and configuration updates to support extremely large model artifacts.

Features:

- [Deployments] Add the `mlflow.deployments.openai` API to simplify direct access to OpenAI services through the deployments API (#10473, @prithvikannan)
- [Server-infra] Add a new environment variable that permits disabling http redirects within the Tracking Server for enhanced security in publicly accessible tracking server deployments (#10673, @daniellok-db)
- [Artifacts] Add environment variable configurations for both Multi-part upload and Multi-part download that permits modifying the per-chunk size to support extremely large model artifacts (#10648, @harupy)

Security fixes:

- [Server-infra] Disable the ability to inject malicious code via manipulated YAML files by forcing YAML rendering to be performed in a secure Sandboxed mode (#10676, @BenWilson2, #10640, @harupy)
- [Artifacts] Prevent path traversal attacks when querying artifact URI locations by disallowing `..` path traversal queries (#10653, @B-Step62)
- [Data] Prevent a mechanism for conducting a malicious file traversal attack on Windows when using tracking APIs that interface with `HTTPDatasetSource` (#10647, @BenWilson2)
- [Artifacts] Prevent a potential path traversal attack vector via encoded url traversal paths by decoding paths prior to evaluation (#10650, @B-Step62)
- [Artifacts] Prevent the ability to conduct path traversal attacks by enforcing the use of sanitized paths with the tracking server (#10666, @harupy)
- [Artifacts] Prevent path traversal attacks when using an FTP server as a backend store by enforcing base path declarations prior to accessing user-supplied paths (#10657, @harupy)

Documentation updates:

- [Docs] Add an end-to-end tutorial for RAG creation and evaluation (#10661, @AbeOmor)
- [Docs] Add Tensorflow landing page (#10646, @chenmoneygithub)
- [Deployments / Tracking] Add endpoints to LLM evaluation docs (#10660, @prithvikannan)
- [Examples] Add retriever evaluation tutorial for LangChain and improve the Question Generation tutorial notebook (#10419, @liangz1)

Small bug fixes and documentation updates:

#10677, #10636, @serena-ruan; #10652, #10649, #10641, @harupy; #10643, #10632, @BenWilson2

## 2.9.1 (2023-12-07)

MLflow 2.9.1 is a patch release, containing a critical bug fix related to loading `pyfunc` models that were saved in previous versions of MLflow.

Bug fixes:

- [Models] Revert Changes to PythonModel that introduced loading issues for models saved in earlier versions of MLflow (#10626, @BenWilson2)

Small bug fixes and documentation updates:

#10625, @BenWilson2

## 2.9.0 (2023-12-05)

MLflow 2.9.0 includes several major features and improvements.

MLflow AI Gateway deprecation (#10420, @harupy):

The feature previously known as MLflow AI Gateway has been moved to utilize [the MLflow deployments API](https://mlflow.org/docs/latest/llms/deployments/index.html).
For guidance on migrating from the AI Gateway to the new deployments API, please see the [MLflow AI Gateway Migration Guide](https://mlflow.org/docs/latest/llms/gateway/migration.html.

MLflow Tracking docs overhaul (#10471, @B-Step62):

[The MLflow tracking docs](https://mlflow.org/docs/latest/tracking.html) have been overhauled. We'd like your feedback on the new tracking docs!

Security fixes:

Three security patches have been filed with this release and CVE's have been issued with the details involved in the security patch and potential attack vectors. Please review and update your tracking server deployments if your tracking server is not securely deployed and has open access to the internet.

- Sanitize `path` in `HttpArtifactRepository.list_artifacts` (#10585, @harupy)
- Sanitize `filename` in `Content-Disposition` header for `HTTPDatasetSource` (#10584, @harupy).
- Validate `Content-Type` header to prevent POST XSS (#10526, @B-Step62)

Features:

- [Tracking] Use `backoff_jitter` when making HTTP requests (#10486, @ajinkyavbhandare)
- [Tracking] Add default `aggregate_results` if the score type is numeric in `make_metric` API (#10490, @sunishsheth2009)
- [Tracking] Add string type of score types for metric value for genai (#10307, @sunishsheth2009)
- [Artifacts] Support multipart upload for for proxy artifact access (#9521, @harupy)
- [Models] Support saving `torch_dtype` for transformers models (#10586, @serena-ruan)
- [Models] Add built-in metric `ndcg_at_k` to retriever evaluation (#10284, @liangz1)
- [Model Registry] Implement universal `copy_model_version` (#10308, @jerrylian-db)
- [Models] Support saving/loading `RunnableSequence`, `RunnableParallel`, and `RunnableBranch` (#10521, #10611, @serena-ruan)

Bug fixes:

- [Tracking] Resume system metrics logging when resuming an existing run (#10312, @chenmoneygithub)
- [UI] Fix incorrect sorting order in line chart (#10553, @B-Step62)
- [UI] Remove extra whitespace in git URLs (#10506, @mrplants)
- [Models] Make spark_udf use NFS to broadcast model to spark executor on databricks runtime and spark connect mode (#10463, @WeichenXu123)
- [Models] Fix promptlab pyfunc models not working for chat routes (#10346, @daniellok-db)

Documentation updates:

- [Docs] Add a quickstart guide for Tensorflow (#10398, @chenmoneygithub)
- [Docs] Improve the parameter tuning guide (#10344, @chenmoneygithub)
- [Docs] Add a guide for system metrics logging (#10429, @chenmoneygithub)
- [Docs] Add instructions on how to configure credentials for Azure OpenAI (#10560, @BenWilson2)
- [Docs] Add docs and tutorials for Sentence Transformers flavor (#10476, @BenWilson2)
- [Docs] Add tutorials, examples, and guides for Transformers Flavor (#10360, @BenWilson2)

Small bug fixes and documentation updates:

#10567, #10559, #10348, #10342, #10264, #10265, @B-Step62; #10595, #10401, #10418, #10394, @chenmoneygithub; #10557, @dan-licht; #10584, #10462, #10445, #10434, #10432, #10412, #10411, #10408, #10407, #10403, #10361, #10340, #10339, #10310, #10276, #10268, #10260, #10224, #10214, @harupy; #10415, @jessechancy; #10579, #10555, @annzhang-db; #10540, @wllgrnt; #10556, @smurching; #10546, @mbenoit29; #10534, @gabrielfu; #10532, #10485, #10444, #10433, #10375, #10343, #10192, @serena-ruan; #10480, #10416, #10173, @jerrylian-db; #10527, #10448, #10443, #10442, #10441, #10440, #10439, #10381, @prithvikannan; #10509, @keenranger; #10508, #10494, @WeichenXu123; #10489, #10266, #10210, #10103, @TomeHirata; #10495, #10435, #10185, @daniellok-db; #10319, @michael-berk; #10417, @bbqiu; #10379, #10372, #10282, @BenWilson2; #10297, @KonakanchiSwathi; #10226, #10223, #10221, @milinddethe15; #10222, @flooxo; #10590, @letian-w;

## 2.8.1 (2023-11-14)

MLflow 2.8.1 is a patch release, containing some critical bug fixes and an update to our continued work on reworking our docs.

Notable details:

- The API `mlflow.llm.log_predictions` is being marked as deprecated, as its functionality has been incorporated into `mlflow.log_table`. This API will be removed in the 2.9.0 release. (#10414, @dbczumar)

Bug fixes:

- [Artifacts] Fix a regression in 2.8.0 where downloading a single file from a registered model would fail (#10362, @BenWilson2)
- [Evaluate] Fix the `Azure OpenAI` integration for `mlflow.evaluate` when using LLM `judge` metrics (#10291, @prithvikannan)
- [Evaluate] Change `Examples` to optional for the `make_genai_metric` API (#10353, @prithvikannan)
- [Evaluate] Remove the `fastapi` dependency when using `mlflow.evaluate` for LLM results (#10354, @prithvikannan)
- [Evaluate] Fix syntax issues and improve the formatting for generated prompt templates (#10402, @annzhang-db)
- [Gateway] Fix the Gateway configuration validator pre-check for OpenAI to perform instance type validation (#10379, @BenWilson2)
- [Tracking] Fix an intermittent issue with hanging threads when using asynchronous logging (#10374, @chenmoneygithub)
- [Tracking] Add a timeout for the `mlflow.login()` API to catch invalid hostname configuration input errors (#10239, @chenmoneygithub)
- [Tracking] Add a `flush` operation at the conclusion of logging system metrics (#10320, @chenmoneygithub)
- [Models] Correct the prompt template generation logic within the Prompt Engineering UI so that the prompts can be used in the Python API (#10341, @daniellok-db)
- [Models] Fix an issue in the `SHAP` model explainability functionality within `mlflow.shap.log_explanation` so that duplicate or conflicting dependencies are not registered when logging (#10305, @BenWilson2)

Documentation updates:

- [Docs] Add MLflow Tracking Quickstart (#10285, @BenWilson2)
- [Docs] Add tracking server configuration guide (#10241, @chenmoneygithub)
- [Docs] Refactor and improve the model deployment quickstart guide (#10322, @prithvikannan)
- [Docs] Add documentation for system metrics logging (#10261, @chenmoneygithub)

Small bug fixes and documentation updates:

#10367, #10359, #10358, #10340, #10310, #10276, #10277, #10247, #10260, #10220, #10263, #10259, #10219, @harupy; #10313, #10303, #10213, #10272, #10282, #10283, #10231, #10256, #10242, #10237, #10238, #10233, #10229, #10211, #10231, #10256, #10242, #10238, #10237, #10229, #10233, #10211, @BenWilson2; #10375, @serena-ruan; #10330, @Haxatron; #10342, #10249, #10249, @B-Step62; #10355, #10301, #10286, #10257, #10236, #10270, #10236, @prithvikannan; #10321, #10258, @jerrylian-db; #10245, @jessechancy; #10278, @daniellok-db; #10244, @gabrielfu; #10226, @milinddethe15; #10390, @bbqiu; #10232, @sunishsheth2009

## 2.8.0 (2023-10-28)

MLflow 2.8.0 includes several notable new features and improvements

- The MLflow Evaluate API has had extensive feature development in this release to support LLM workflows and multiple new evaluation modalities. See the new documentation, guides, and tutorials for MLflow LLM Evaluate to learn more.
- The MLflow Docs modernization effort has started. You will see a very different look and feel to the docs when visiting them, along with a batch of new tutorials and guides. More changes will be coming soon to the docs!
- 4 new LLM providers have been added! Google PaLM 2, AWS Bedrock, AI21 Labs, and HuggingFace TGI can now be configured and used within the AI Gateway. Learn more in the new AI Gateway docs!

Features:

- [Gateway] Add support for AWS Bedrock as a provider in the AI Gateway (#9598, @andrew-christianson)
- [Gateway] Add support for Huggingface Text Generation Inference as a provider in the AI Gateway (#10072, @SDonkelaarGDD)
- [Gateway] Add support for Google PaLM 2 as a provider in the AI Gateway (#9797, @arpitjasa-db)
- [Gateway] Add support for AI21labs as a provider in the AI Gateway (#9828, #10168, @zhe-db)
- [Gateway] Introduce a simplified method for setting the configuration file location for the AI Gateway via environment variable (#9822, @danilopeixoto)
- [Evaluate] Introduce default provided LLM evaluation metrics for MLflow evaluate (#9913, @prithvikannan)
- [Evaluate] Add support for evaluating inference datasets in MLflow evaluate (#9830, @liangz1)
- [Evaluate] Add support for evaluating single argument functions in MLflow evaluate (#9718, @liangz1)
- [Evaluate] Add support for Retriever LLM model type evaluation within MLflow evaluate (#10079, @liangz1)
- [Models] Add configurable parameter for external model saving in the ONNX flavor to address a regression (#10152, @daniellok-db)
- [Models] Add support for saving inference parameters in a logged model's input example (#9655, @serena-ruan)
- [Models] Add support for `completions` in the OpenAI flavor (#9838, @santiagxf)
- [Models] Add support for inference parameters for the OpenAI flavor (#9909, @santiagxf)
- [Models] Introduce support for configuration arguments to be specified when loading a model (#9251, @santiagxf)
- [Models] Add support for integrated Azure AD authentication for the OpenAI flavor (#9704, @santiagxf)
- [Models / Scoring] Introduce support for model training lineage in model serving (#9402, @M4nouel)
- [Model Registry] Introduce the `copy_model_version` client API for copying model versions across registered models (#9946, #10078, #10140, @jerrylian-db)
- [Tracking] Expand the limits of parameter value length from 500 to 6000 (#9709, @serena-ruan)
- [Tracking] Introduce support for Spark 3.5's SparkConnect mode within MLflow to allow logging models created using this operation mode of Spark (#9534, @WeichenXu123)
- [Tracking] Add support for logging system metrics to the MLflow fluent API (#9557, #9712, #9714, @chenmoneygithub)
- [Tracking] Add callbacks within MLflow for Keras and Tensorflow (#9454, #9637, #9579, @chenmoneygithub)
- [Tracking] Introduce a fluent login API for Databricks within MLflow (#9665, #10180, @chenmoneygithub)
- [Tracking] Add support for customizing auth for http requests from the MLflow client via a plugin extension (#10049, @lu-ohai)
- [Tracking] Introduce experimental asynchronous logging support for metrics, params, and tags (#9705, @sagarsumant)
- [Auth] Modify the behavior of user creation in MLflow Authentication so that only admins can create new users (#9700, @gabrielfu)
- [Artifacts] Add support for using `xethub` as an artifact store via a plugin extension (#9957, @Kelton8Z)
- [UI] Add new opt-in Model Registry UI that supports model aliases and tags (#10163, @hubertzub-db, @jerrylian-db)

Bug fixes:

- [Evaluate] Fix a bug with Azure OpenAI configuration usage within MLflow evaluate (#9982, @sunishsheth2009)
- [Models] Fix a data consistency issue when saving models that have been loaded in heterogeneous memory configuration within the transformers flavor (#10087, @BenWilson2)
- [Models] Fix an issue in the transformers flavor for complex input types by adding dynamic dataframe typing (#9044, @wamartin-aml)
- [Models] Fix an issue in the langchain flavor to provide support for chains with multiple outputs (#9497, @bbqiu)
- [Docker] Fix an issue with Docker image generation by changing the default env-manager to virtualenv (#9938, @Beramos)
- [Auth] Fix an issue with complex passwords in MLflow Auth to support a richer character set range (#9760, @dotdothu)
- [R] Fix a bug with configuration access when running MLflow R in Databricks (#10117, @zacdav-db)

Documentation updates:

- [Docs] Introduce the first phase of a larger documentation overhaul (#10197, @BenWilson2)
- [Docs] Add guide for LLM eval (#10058, #10199, @chenmoneygithub)
- [Docs] Add instructions on how to force single file serialization within the onnx flavor's save and log functions (#10178, @BenWilson2)
- [Docs] Add documentation for the relevance metric for MLflow evaluate (#10170, @sunishsheth2009)
- [Docs] Add a style guide for the contributing guide for how to structure pydoc strings (#9907, @mberk06)
- [Docs] Fix issues with the pytorch lightning autolog code example (#9964, @chenmoneygithub)
- [Docs] Update the example for `mlflow.data.from_numpy()` (#9885, @chenmoneygithub)
- [Docs] Add clear instructions for installing MLflow within R (#9835, @darshan8850)
- [Docs] Update model registry documentation to add content regarding support for model aliases (#9721, @jerrylian-db)

Small bug fixes and documentation updates:

#10202, #10189, #10188, #10159, #10175, #10165, #10154, #10083, #10082, #10081, #10071, #10077, #10070, #10053, #10057, #10055, #10020, #9928, #9929, #9944, #9979, #9923, #9842, @annzhang-db; #10203, #10196, #10172, #10176, #10145, #10115, #10107, #10054, #10056, #10018, #9976, #9999, #9998, #9995, #9978, #9973, #9975, #9972, #9974, #9960, #9925, #9920, @prithvikannan; #10144, #10166, #10143, #10129, #10059, #10123, #9555, #9619, @bbqiu; #10187, #10191, #10181, #10179, #10151, #10148, #10126, #10119, #10099, #10100, #10097, #10089, #10096, #10091, #10085, #10068, #10065, #10064, #10060, #10023, #10030, #10028, #10022, #10007, #10006, #9988, #9961, #9963, #9954, #9953, #9937, #9932, #9931, #9910, #9901, #9852, #9851, #9848, #9847, #9841, #9844, #9825, #9820, #9806, #9802, #9800, #9799, #9790, #9787, #9791, #9788, #9785, #9786, #9784, #9754, #9768, #9770, #9753, #9697, #9749, #9747, #9748, #9751, #9750, #9729, #9745, #9735, #9728, #9725, #9716, #9694, #9681, #9666, #9643, #9641, #9621, #9607, @harupy; #10200, #10201, #10142, #10139, #10133, #10090, #10086, #9934, #9933, #9845, #9831, #9794, #9692, #9627, #9626, @chenmoneygithub; #10110, @wenfeiy-db; #10195, #9895, #9880, #9679, @BenWilson2; #10174, #10177, #10109, #9706, @jerrylian-db; #10113, #9765, @smurching; #10150, #10138, #10136, @dbczumar; #10153, #10032, #9986, #9874, #9727, #9707, @serena-ruan; #10155, @shaotong-db; #10160, #10131, #10048, #10024, #10017, #10016, #10002, #9966, #9924, @sunishsheth2009; #10121, #10116, #10114, #10102, #10098, @B-Step62; #10095, #10026, #9991, @daniellok-db; #10050, @Dennis40816; #10062, #9868, @Gekko0114; #10033, @Anushka-Bhowmick; #9983, #10004, #9958, #9926, #9690, @liangz1; #9997, #9940, #9922, #9919, #9890, #9888, #9889, #9810, @TomeHirata; #9994, #9970, #9950, @lightnessofbein; #9965, #9677, @ShorthillsAI; #9906, @jessechancy; #9942, #9771, @Sai-Suraj-27; #9902, @remyleone; #9892, #9865, #9866, #9853, @montanarograziano; #9875, @Raghavan-B; #9858, @Salz0; #9878, @maksboyarin; #9882, @lukasz-gawron; #9827, @Bncer; #9819, @gabrielfu; #9792, @harshk461; #9726, @Chiragasourabh; #9663, @Abhishek-TyRnT; #9670, @mberk06; #9755, @simonlsk; #9757, #9775, #9776, #9774, @AmirAflak; #9782, @garymm; #9756, @issamarabi; #9645, @shichengzhou-db; #9671, @zhe-db; #9660, @mingyu89; #9575, @akshaya-a; #9629, @pnacht; #9876, @C-K-Loan

## 2.7.1 (2023-09-17)

MLflow 2.7.1 is a patch release containing the following features, bug fixes and changes:

Features:

- [Gateway / Databricks] Add the `set_limits` and `get_limits` APIs for AI Gateway routes within Databricks (#9516, @zhe-db)
- [Artifacts / Databricks] Add support for parallelized download and upload of artifacts within Unity Catalog (#9498, @jerrylian-db)

Bug fixes:

- [Models / R] Fix a critical bug with the `R` client that prevents models from being loaded (#9624, @BenWilson2)
- [Artifacts / Databricks] Disable multi-part download functionality for UC Volumes local file destination when downloading models (#9631, @BenWilson2)

Small bug fixes and documentation updates:

#9640, @annzhang-db; #9622, @harupy

## 2.7.0 (2023-09-12)

MLflow 2.7.0 includes several major features and improvements

- [UI / Gateway] We are excited to announce the Prompt Engineering UI. This new addition offers a suite of tools tailored for efficient prompt development, testing, and evaluation for LLM use cases. Integrated directly into the MLflow AI Gateway, it provides a seamless experience for designing, tracking, and deploying prompt templates. To read about this new feature, see the documentation at https://mlflow.org/docs/latest/llms/prompt-engineering.html (#9503, @prithvikannan)

Features:

- [Gateway] Introduce `MosaicML` as a supported provider for the MLflow `AI Gateway` (#9459, @arpitjasa-db)
- [Models] Add support for using a snapshot download location when loading a `transformers` model as `pyfunc` (#9362, @serena-ruan)
- [Server-infra] Introduce plugin support for MLflow `Tracking Server` authentication (#9191, @barrywhart)
- [Artifacts / Model Registry] Add support for storing artifacts using the `R2` backend (#9490, @shichengzhou-db)
- [Artifacts] Improve upload and download performance for Azure-based artifact stores (#9444, @jerrylian-db)
- [Sagemaker] Add support for deploying models to Sagemaker Serverless inference endpoints (#9085, @dogeplusplus)

Bug fixes:

- [Gateway] Fix a credential expiration bug by re-resolving `AI Gateway` credentials before each request (#9518, @dbczumar)
- [Gateway] Fix a bug where `search_routes` would raise an exception when no routes have been defined on the `AI Gateway` server (#9387, @QuentinAmbard)
- [Gateway] Fix compatibility issues with `pydantic` 2.x for `AI gateway` (#9339, @harupy)
- [Gateway] Fix an initialization issue in the `AI Gateway` that could render MLflow nonfunctional at import if dependencies were conflicting. (#9337, @BenWilson2)
- [Artifacts] Fix a correctness issue when downloading large artifacts to `fuse mount` paths on `Databricks` (#9545, @BenWilson2)

Documentation updates:

- [Docs] Add documentation for the `Giskard` community plugin for `mlflow.evaluate` (#9183, @rabah-khalek)

Small bug fixes and documentation updates:

#9605, #9603, #9602, #9595, #9597, #9587, #9590, #9588, #9586, #9584, #9583, #9582, #9581, #9580, #9577, #9546, #9566, #9569, #9562, #9564, #9561, #9528, #9506, #9503, #9492, #9491, #9485, #9445, #9430, #9429, #9427, #9426, #9424, #9421, #9419, #9409, #9408, #9407, #9394, #9389, #9395, #9393, #9390, #9370, #9356, #9359, #9357, #9345, #9340, #9328, #9329, #9326, #9304, #9325, #9323, #9322, #9319, #9314, @harupy; #9568, #9520, @dbczumar; #9593, @jerrylian-db; #9574, #9573, #9480, #9332, #9335, @BenWilson2; #9556, @shichengzhou-db; #9570, #9540, #9533, #9517, #9354, #9453, #9338, @prithvikannan; #9565, #9560, #9536, #9504, #9476, #9481, #9450, #9466, #9418, #9397, @serena-ruan; #9489, @dnerini; #9512, #9479, #9355, #9351, #9289 @chenmoneygithub; #9488, @bbqiu; #9474, @apurva-koti; #9505, @arpitjasa-db; #9261, @donour; #9336, #9414, #9353, @mberk06; #9451, @Bncer; #9432, @barrywhart; #9347, @GraceBrigham; #9428, #9420, #9406, @WeichenXu123; #9410, @aloahPGF; #9396, #9384, #9372, @Godwin-T; #9373, @fabiansefranek; #9382, @Sai-Suraj-27; #9378, @saidattu2003; #9375, @Increshi; #9358, @smurching; #9366, #9330, @Dev-98; #9364, @Sandeep1005; #9349, #9348, @AmirAflak; #9308, @danilopeixoto; #9596, @ShorthillsAI; #9567, @Beramos; #9524, @rabah-khalek; #9312, @dependabot[bot]

## 2.6.0 (2023-08-15)

MLflow 2.6.0 includes several major features and improvements

Features:

- [Models / Scoring] Add support for passing extra params during inference for PyFunc models (#9068, @serena-ruan)
- [Gateway] Add support for MLflow serving to MLflow AI Gateway (#9199, @BenWilson2)
- [Tracking] Support `save_kwargs` for `mlflow.log_figure` to specify extra options when saving a figure (#9179, @stroblme)
- [Artifacts] Display progress bars when uploading/download artifacts (#9195, @serena-ruan)
- [Models] Add support for logging LangChain's retriever models (#8808, @liangz1)
- [Tracking] Add support to log customized tags to runs created by autologging (#9114, @thinkall)

Bug fixes:

- [Models] Fix `text_pair` functionality for transformers `TextClassification` pipelines (#9215, @BenWilson2)
- [Models] Fix LangChain compatibility with SQLDatabase (#9192, @dbczumar)
- [Tracking] Remove patching `sklearn.metrics.get_scorer_names` in `mlflow.sklearn.autolog` to avoid duplicate logging (#9095, @WeichenXu123)

Documentation updates:

- [Docs / Examples] Add examples and documentation for MLflow AI Gateway support for MLflow model serving (#9281, @BenWilson2)
- [Docs / Examples] Add `sentence-transformers` doc & example (#9047, @es94129)

Deprecation:

- [Models] The `mlflow.mleap` module has been marked as deprecated and will be removed in a future release (#9311, @BenWilson2)

Small bug fixes and documentation updates:

#9309, #9252, #9198, #9189, #9186, #9184, @BenWilson2; #9307, @AmirAflak; #9285, #9126, @dependabot[bot]; #9302, #9209, #9194, #9187, #9175, #9177, #9163, #9161, #9129, #9123, #9053, @serena-ruan; #9305, #9303, #9271, @KekmaTime; #9300, #9299, @itsajay1029; #9294, #9293, #9274, #9268, #9264, #9246, #9255, #9253, #9254, #9245, #9202, #9243, #9238, #9234, #9233, #9227, #9226, #9223, #9224, #9222, #9225, #9220, #9208, #9212, #9207, #9203, #9201, #9200, #9154, #9146, #9147, #9153, #9148, #9145, #9136, #9132, #9131, #9128, #9121, #9124, #9125, #9108, #9103, #9100, #9098, #9101, @harupy; #9292, @Aman123lug; #9290, #9164, #9157, #9086, @Bncer; #9291, @kunal642; #9284, @NavneetSinghArora; #9286, #9262, #9142, @smurching; #9267, @tungbq; #9258, #9250, @Kunj125; #9167, #9139, #9120, #9118, #9097, @viktoriussuwandi; #9244, #9240, #9239, @Sai-Suraj-27; #9221, #9168, #9130, @gabrielfu; #9218, @tjni; #9216, @Rukiyav; #9158, #9051, @EdAbati; #9211, @scarlettrobe; #9049, @annzhang-db; #9140, @kriscon-db; #9141, @xAIdrian; #9135, @liangz1; #9067, @jmmonteiro; #9112, @WeichenXu123; #9106, @shaikmoeed; #9105, @Ankit8848; #9104, @arnabrahman

## 2.5.0 (2023-07-17)

MLflow 2.5.0 includes several major features and improvements:

- [MLflow AI Gateway] We are excited to announce the release of MLflow AI Gateway, a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a standardized interface that simplifies the interaction with these services and delivers centralized, secure management of credentials. To get started with MLflow AI Gateway, check out the docs at https://mlflow.org/docs/latest/gateway/index.html. (#8694, @harupy, @BenWilson2, @dbczumar)
- [Auth]: We are excited to announce the release of authentication and authorization support for MLflow Tracking and the MLflow Model Registry, providing integrated access control capabilities to both services. To get started, check out the docs at https://mlflow.org/docs/latest/auth/index.html. (#9000, #8975, #8626, #8837, #8841, @gabrielfu, @harupy)

Features:

- [Models] Add Support to the LangChain flavor for chains that contain unserializable components (#8736, @liangz1)
- [Scoring] Infer spark udf return type from model output schema (#8934, @WeichenXu123)
- [Models] Add support for automated signature inference (#8860, #8782 #8795, #8725, @jerrylian-db)

Bug fixes:

- [Security] Improve robustness to LFI attacks on Windows by enhancing path validation (#8999, @serena-ruan)
  - If you are using `mlflow server` or `mlflow ui` on Windows, we recommend upgrading to MLflow 2.5.0 as soon as possible.
- [Scoring] Support nullable array type values as spark_udf return values (#9014, @WeichenXu123)
- [Models] Revert cache deletion of system modules when adding custom model code to the system path (#8722, @trungn1)
- [Models] add micro version to mlflow version pinning (#8687, @C-K-Loan)
- [Artifacts] Prevent manually deleted artifacts from causing artifact garbage collection to fail (#8498, @PenHsuanWang)

Documentation updates:

- [Docs] Update .push_model_to_sagemaker docs (#8851, @pdifranc)
- [Docs] Fix invalid link for Azure ML documentation (#8800, @dunnkers)
- [Artifacts / Docs / Models / Projects] Adds information on the OCI MLflow plugins for seamless integration with Oralce Cloud Infrastructure services. (#8707, @mrDzurb)

Deprecation:

- [Models] Deprecate the `gluon` model flavor. The `mlflow.gluon` module will be removed in a future release. (#8968, @harupy)

Small bug fixes and documentation updates:

#9069, #9056, #9055, #9054, #9048, #9043, #9035, #9034, #9037, #9038, #8993, #8966, #8985, @BenWilson2; #9039, #9036, #8902, #8924, #8866, #8861, #8810, #8761, #8544, @jerrylian-db; #8903, @smurching; #9080, #9079, #9078, #9076, #9075, #9074, #9071, #9063, #9062, #9032, #9031, #9027, #9023, #9022, #9020, #9005, #8994, #8979, #8983, #8984, #8982, #8970, #8962, #8969, #8968, #8959, #8960, #8958, #8956, #8955, #8954, #8949, #8950, #8952, #8948, #8946, #8947, #8943, #8944, #8916, #8917, #8933, #8929, #8932, #8927, #8930, #8925, #8921, #8873, #8915, #8909, #8908, #8911, #8910, #8907, #8906, #8898, #8893, #8889, #8892, #8891, #8887, #8875, #8876, #8882, #8874, #8868, #8872, #8869, #8828, #8852, #8857, #8853, #8854, #8848, #8850, #8840, #8835, #8832, #8831, #8830, #8829, #8839, #8833, #8838, #8819, #8814, #8825, #8818, #8787, #8775, #8749, #8766, #8756, #8753, #8751, #8748, #8744, #8731, #8717, #8730, #8691, #8720, #8723, #8719, #8688, #8721, #8715, #8716, #8718, #8696, #8698, #8692, #8693, #8690, @harupy; #9030, @AlimurtuzaCodes; #9029, #9025, #9021, #9013, @viktoriussuwandi; #9010, @Bncer; #9011, @Pecunia201; #9007, #9003, @EdAbati; #9002, @prithvikannan; #8991, #8867, @AveshCSingh; #8951, #8896, #8888, #8849, @gabrielfu; #8913, #8885, #8871, #8870, #8788, #8772, #8771, @serena-ruan; #8879, @maciejskorski; #7752, @arunkumarkota; #9083, #9081, #8765, #8742, #8685, #8682, #8683, @dbczumar; #8791, @mhattingpete; #8739, @yunpark93

## 2.4.2 (2023-07-10)

MLflow 2.4.2 is a patch release containing the following bug fixes and changes:

Bug fixes:

- [Models] Add compatibility for legacy transformers serialization (#8964, @BenWilson2)
- [Models] Fix downloading MLmodel files from alias-based models:/ URIs (#8764, @smurching)
- [Models] Fix reading model flavor config from URI for models in UC (#8728, @smurching)
- [Models] Support `feature_deps` in ModelVersion creation for UC (#8867, #8815, @AveshCSingh)
- [Models] Add support for listing artifacts in UC model registry artifact repo (#8803, @smurching)
- [Core] Include resources for recipes in mlflow-skinny (#8895, @harupy)
- [UI] Enable datasets tracking UI (#8886, @harupy)
- [Artifacts] Use `MLFLOW_ENABLE_MULTIPART_DOWNLOAD` in `DatabricksArtifactRepository` (#8884, @harupy)

Documentation updates:

- [Examples / Docs] Add question-answering and summarization examples and docs with LLMs (#8695, @dbczumar)
- [Examples / Docs] Add johnsnowlabs flavor example and doc (#8689, @C-K-Loan)

Small bug fixes and documentation updates:

#8966, @BenWilson2; #8881, @harupy; #8846, #8760, @smurching

## 2.4.1 (2023-06-09)

MLflow 2.4.1 is a patch release containing the following features, bug fixes and changes:

Features:

- [Tracking] Extend SearchRuns to support datasets (#8622, @prithvikannan)
- [Models] Add an `mlflow.johnsnowlabs` flavor for the `johnsnowlabs` package (#8556, @C-K-Loan)
- [Models] Add a warning for duplicate pip requirements specified in `save_model` and `log_model` for the `transformers` flavor (#8678, @BenWilson2)

Bug fixes:

- [Security] Improve robustness to LFI attacks (#8648, @serena-ruan)
  - If you are using `mlflow server` or `mlflow ui`, we recommend upgrading to MLflow 2.4.1 as soon as possible.
- [Models] Fix an issue with `transformers` serialization for ModelCards that contain invalid characters (#8652, @BenWilson2)
- [Models] Fix connection pooling deadlocks that occurred during large file downloads (#8682, @dbczumar; #8660, @harupy)

Small bug fixes and documentation updates:

#8677, #8674, #8646, #8647, @dbczumar; #8654, #8653, #8660, #8650, #8642, #8636, #8599, #8637, #8608, #8633, #8623, #8628, #8619, @harupy; #8655, #8609, @BenWilson2; #8648, @serena-ruan; #8521, @ka1mar; #8638, @smurching; #8634, @PenHsuanWang

## 2.4.0 (2023-06-06)

MLflow 2.4.0 includes several major features and improvements

Features:

- [Tracking] Introduce dataset tracking APIs: `mlflow.data` and `mlflow.log_input()` (#8186, @prithvikannan)
- [Tracking] Add `mlflow.log_table()` and `mlflow.load_table()` APIs for logging evaluation tables (#8523, #8467, @sunishsheth2009)
- [Tracking] Introduce `mlflow.get_parent_run()` fluent API (#8493, @annzhang-db)
- [Tracking / Model Registry] Re-introduce faster artifact downloads on Databricks (#8352, @dbczumar; #8561, @harupy)
- [UI] Add dataset tracking information to MLflow Tracking UI (#8602, @prithvikannan, @hubertzub-db)
- [UI] Introduce Artifact View for comparing inputs, outputs, and metadata across models (#8602, @hubertzub-db)
- [Models] Extend `mlflow.evaluate()` to support LLM tasks (#8484, @harupy)
- [Models] Support logging subclasses of `Chain` and `LLMChain` in `mlflow.langchain` flavor (#8453, @liangz1)
- [Models] Add support for LangChain Agents to the `mlflow.langchain` flavor (#8297, @sunishsheth2009)
- [Models] Add a `mlflow.sentence_transformers` flavor for SentenceTransformers (#8479, @BenWilson2; #8547, @Loquats)
- [Models] Add support for multi-GPU inference and efficient weight loading for `mlflow.transformers` flavor (#8448, @ankit-db)
- [Models] Support the `max_shard_size` parameter in the `mlflow.transformers` flavor (#8567, @wenfeiy-db)
- [Models] Add support for audio transcription pipelines in the `mlflow.transformers` flavor (#8464, @BenWilson2)
- [Models] Add support for audio classification to `mlflow.transformers` flavor (#8492, @BenWilson2)
- [Models] Add support for URI inputs in audio models logged with the `mlflow.transformers` flavor (#8495, @BenWilson2)
- [Models] Add support for returning classifier scores in `mlflow.transformers` pyfunc outputs (#8512, @BenWilson2)
- [Models] Support optional inputs in model signatures (#8438, @apurva-koti)
- [Models] Introduce an `mlflow.models.set_signature()` API to set the signature of a logged model (#8476, @jerrylian-db)
- [Models] Persist ONNX Runtime InferenceSession options when logging a model with `mlflow.onnx.log_model()` (#8433, @leqiao-1)

Bug fixes:

- [Tracking] Terminate Spark callback server when Spark Autologging is disabled or Spark Session is shut down (#8508, @WeichenXu123)
- [Tracking] Fix compatibility of `mlflow server` with `Flask<2.0` (#8463, @kevingreer)
- [Models] Convert `mlflow.transformers` pyfunc scalar string output to list of strings during batch inference (#8546, @BenWilson2)
- [Models] Fix a bug causing outdated pyenv versions to be installed by `mlflow models build-docker` (#8488, @Hellzed)
- [Model Registry] Remove aliases from storage when a Model Version is deleted (#8459, @arpitjasa-db)

Documentation updates:

- [Docs] Publish a new MLOps Quickstart for model selection and deployment (#8462, @lobrien)
- [Docs] Add MLflavors library to Community Model Flavors documentation (#8420, @benjaminbluhm)
- [Docs] Add documentation for Registered Model Aliases (#8445, @arpitjasa-db)
- [Docs] Fix errors in documented `mlflow models` CLI command examples (#8480, @vijethmoudgalya)

Small bug fixes and documentation updates:

#8611, #8587, @dbczumar; #8617, #8620, #8615, #8603, #8604, #8601, #8596, #8598, #8597, #8589, #8580, #8581, #8575, #8582, #8577, #8576, #8578, #8561, #8568, #8551, #8528, #8550, #8489, #8530, #8534, #8533, #8532, #8524, #8520, #8517, #8516, #8515, #8514, #8506, #8503, #8500, #8504, #8496, #8486, #8485, #8468, #8471, #8473, #8470, #8458, #8447, #8446, #8434, @harupy; #8607, #8538, #8513, #8452, #8466, #8465, @serena-ruan; #8586, #8595, @prithvikannan; #8593, #8541, @kriscon-db; #8592, #8566, @annzhang-db; #8588, #8565, #8559, #8537, @BenWilson2; #8545, @apurva-koti; #8564, @DavidSpek; #8436, #8490, @jerrylian-db; #8505, @eliaskoromilas; #8483, @WeichenXu123; #8472, @leqiao-1; #8429, @jinzhang21; #8581, #8548, #8499, @gabrielfu;

## 2.3.2 (2023-05-12)

MLflow 2.3.2 is a patch release containing the following features, bug fixes and changes:

Features:

- [Models] Add GPU support for `transformers` models `pyfunc` inference and serving (#8375, @ankit-db)
- [Models] Disable autologging functionality for non-relevant models when training a `transformers` model (#8405, @BenWilson2)
- [Models] Add support for preserving and overriding `torch_dtype` values in `transformers` pipelines (#8421, @BenWilson2)
- [Models] Add support for `Feature Extraction` pipelines in the `transformers` flavor (#8423, @BenWilson2)
- [Tracking] Add basic HTTP auth support for users, registered models, and experiments permissions (#8286, @gabrielfu)

Bug Fixes:

- [Models] Fix inferred schema issue with `Text2TextGeneration` pipelines in the `transformers` flavor (#8391, @BenWilson2)
- [Models] Change MLflow dependency pinning in logged models from a range value to an exact major and minor version (#8422, @harupy)

Documentation updates:

- [Examples] Add `signature` logging to all examples and documentation (#8410, #8401, #8400, #8387 @jerrylian-db)
- [Examples] Add `sentence-transformers` examples to the `transformers` examples suite (#8425, @BenWilson2)
- [Docs] Add a new MLflow Quickstart documentation page (#8171, @lobrien)
- [Docs] Add a new introduction to MLflow page (#8365, @lobrien)
- [Docs] Add a community model plugin example and documentation for `trubrics` (#8371, @jeffkayne)
- [Docs] Add `gluon` pyfunc example to Model flavor documentation (#8403, @ericvincent18)
- [Docs] Add `statsmodels` pyfunc example to `Models` flavor documentation (#8394, @ericvincent18)

Small bug fixes and documentation updates:

#8415, #8412, #8411, #8355, #8354, #8353, #8348, @harupy; #8374, #8367, #8350, @dbczumar; #8358 @mrkaye97; #8392, #8362, @smurching; #8427, #8408, #8399, #8381, @BenWilson2; #8395, #8390, @jerrylian-db; #8402, #8398, @WeichenXu123; #8377, #8363, @arpitjasa-db; #8385, @prithvikannan; #8418, @Jeukoh;

## 2.3.1 (2023-04-27)

MLflow 2.3.1 is a patch release containing the following bug fixes and changes:

Bug fixes:

- [Security] Fix critical LFI attack vulnerability by disabling the ability to provide relative paths in registered model sources (#8281, @BenWilson2)
  - **If you are using `mlflow server` or `mlflow ui`, we recommend upgrading to MLflow 2.3.1 as soon as possible.** For more details, see https://github.com/mlflow/mlflow/security/advisories/GHSA-xg73-94fp-g449.
- [Tracking] Fix an issue causing file and model uploads to hang on Databricks (#8348, @harupy)
- [Tracking / Model Registry] Fix an issue causing file and model downloads to hang on Databricks (#8350, @dbczumar)
- [Scoring] Fix regression in schema enforcement for model serving when using the `inputs` format for inference (#8326, @BenWilson2)
- [Model Registry] Fix regression in model naming parsing where special characters were not accepted in model names (#8322, @arpitjasa-db)
- [Recipes] Fix card rendering with the pandas profiler to handle columns containing all null values (#8263, @sunishsheth2009)

Documentation updates:

- [Docs] Add an H2O pyfunc usage example to the models documentation (#8292, @ericvincent18)
- [Examples] Add a TensorFlow Core 2.x API usage example (#8235, @dheerajnbhat)

Small bug fixes and documentation updates:

#8324, #8325, @smurching; #8313, @dipanjank; #8323, @liangz1; #8331, #8328, #8319, #8316, #8308, #8293, #8289, #8283, #8284, #8285, #8282, #8241, #8270, #8272, #8271, #8268, @harupy; #8312, #8294, #8295, #8279, #8267, @BenWilson2; #8290, @jinzhang21; #8257, @WeichenXu123; #8307, @arpitjasa-db

## 2.3.0 (2023-04-18)

MLflow 2.3.0 includes several major features and improvements

Features:

- [Models] Introduce a new `transformers` named flavor (#8236, #8181, #8086, @BenWilson2)
- [Models] Introduce a new `openai` named flavor (#8191, #8155, @harupy)
- [Models] Introduce a new `langchain` named flavor (#8251, #8197, @liangz1, @sunishsheth2009)
- [Models] Add support for `Pytorch` and `Lightning` 2.0 (#8072, @shrinath-suresh)
- [Tracking] Add support for logging LLM input, output, and prompt artifacts (#8234, #8204, @sunishsheth2009)
- [Tracking] Add support for HTTP Basic Auth in the MLflow tracking server (#8130, @gabrielfu)
- [Tracking] Add `search_model_versions` to the fluent API (#8223, @mariusschlegel)
- [Artifacts] Add support for parallelized artifact downloads (#8116, @apurva-koti)
- [Artifacts] Add support for parallelized artifact uploads for AWS (#8003, @harupy)
- [Artifacts] Add content type headers to artifact upload requests for the `HttpArtifactRepository` (#8048, @WillEngler)
- [Model Registry] Add alias support for logged models within Model Registry (#8164, #8094, #8055 @arpitjasa-db)
- [UI] Add support for custom domain git providers (#7933, @gusghrlrl101)
- [Scoring] Add plugin support for customization of MLflow serving endpoints (#7757, @jmahlik)
- [Scoring] Add support to MLflow serving that allows configuration of multiple inference workers (#8035, @M4nouel)
- [Sagemaker] Add support for asynchronous inference configuration on Sagemaker (#8009, @thomasbell1985)
- [Build] Remove `shap` as a core dependency of MLflow (#8199, @jmahlik)

Bug fixes:

- [Models] Fix a bug with `tensorflow` autologging for models with multiple inputs (#8097, @jaume-ferrarons)
- [Recipes] Fix a bug with `Pandas` 2.0 updates for profiler rendering of datetime types (#7925, @sunishsheth2009)
- [Tracking] Prevent exceptions from being raised if a parameter is logged with an existing key whose value is identical to the logged parameter (#8038, @AdamStelmaszczyk)
- [Tracking] Fix an issue with deleting experiments in the FileStore backend (#8178, @mariusschlegel)
- [Tracking] Fix a UI bug where the "Source Run" field in the Model Version page points to an incorrect set of artifacts (#8156, @WeichenXu123)
- [Tracking] Fix a bug wherein renaming a run reverts its current lifecycle status to `UNFINISHED` (#8154, @WeichenXu123)
- [Tracking] Fix a bug where a file URI could be used as a model version source (#8126, @harupy)
- [Projects] Fix an issue with MLflow projects that have submodules contained within a project (#8050, @kota-iizuka)
- [Examples] Fix `lightning` hyperparameter tuning examples (#8039, @BenWilson2)
- [Server-infra] Fix bug with Cache-Control headers for static server files (#8016, @jmahlik)

Documentation updates:

- [Examples] Add a new and thorough example for the creation of custom model flavors (#7867, @benjaminbluhm)

Small bug fixes and documentation updates:

#8262, #8252, #8250, #8228, #8221, #8203, #8134, #8040, #7994, #7934, @BenWilson2; #8258, #8255, #8253, #8248, #8247, #8245, #8243, #8246, #8244, #8242, #8240, #8229, #8198, #8192, #8112, #8165, #8158, #8152, #8148, #8144, #8143, #8120, #8107, #8105, #8102, #8088, #8089, #8096, #8075, #8073, #8076, #8063, #8064, #8033, #8024, #8023, #8021, #8015, #8005, #7982, #8002, #7987, #7981, #7968, #7931, #7930, #7929, #7917, #7918, #7916, #7914, #7913, @harupy; #7955, @arjundc-db; #8219, #8110, #8093, #8087, #8091, #8092, #8029, #8028, #8031, @jerrylian-db; #8187, @apurva-koti; #8210, #8001, #8000, @arpitjasa-db; #8161, #8127, #8095, #8090, #8068, #8043, #7940, #7924, #7923, @dbczumar; #8147, @morelen17; #8106, @WeichenXu123; #8117, @eltociear; #8100, @laerciop; #8080, @elado; #8070, @grofte; #8066, @yukimori; #8027, #7998, @liangz1; #7999, @martlaf; #7964, @viditjain99; #7928, @alekseyolg; #7909, #7901, #7844, @smurching; #7971, @n30111; #8012, @mingyu89; #8137, @lobrien; #7992, @robmarkcole; #8263, @sunishsheth2009

## 2.2.2 (2023-03-14)

MLflow 2.2.2 is a patch release containing the following bug fixes:

- [Model Registry] Allow `source` to be a local path within a run's artifact directory if a `run_id` is specified (#7993, @harupy)
- [Model Registry] Fix a bug where a windows UNC path is considered a local path (#7988, @WeichenXu123)
- [Model Registry] Disallow `name` to be a file path in `FileStore.get_registered_model` (#7965, @harupy)

## 2.2.1 (2023-03-02)

MLflow 2.2.1 is a patch release containing the following bug fixes:

- [Model Registry] Fix a bug that caused too many results to be requested by default when calling `MlflowClient.search_model_versions()` (#7935, @dbczumar)
- [Model Registry] Patch for GHSA-xg73-94fp-g449 (#7908, @harupy)
- [Model Registry] Patch for GHSA-wp72-7hj9-5265 (#7965, @harupy)

## 2.2.0 (2023-02-28)

MLflow 2.2.0 includes several major features and improvements

Features:

- [Recipes] Add support for score calibration to the classification recipe (#7744, @sunishsheth2009)
- [Recipes] Add automatic label encoding to the classification recipe (#7711, @sunishsheth2009)
- [Recipes] Support custom data splitting logic in the classification and regression recipes (#7815, #7588, @sunishsheth2009)
- [Recipes] Introduce customizable MLflow Run name prefixes to the classification and regression recipes (#7746, @kamalesh0406; #7763, @sunishsheth2009)
- [UI] Add a new Chart View to the MLflow Experiment Page for model performance insights (#7864, @hubertzub-db, @apurva-koti, @prithvikannan, @ridhimag11, @sunishseth2009, @dbczumar)
- [UI] Modernize and improve parallel coordinates chart for model tuning (#7864, @hubertzub-db, @apurva-koti, @prithvikannan, @ridhimag11, @sunishseth2009, @dbczumar)
- [UI] Add typeahead suggestions to the MLflow Experiment Page search bar (#7864, @hubertzub-db, @apurva-koti, @prithvikannan, @ridhimag11, @sunishseth2009, @dbczumar)
- [UI] Improve performance of Experiments Sidebar for large numbers of experiments (#7804, @jmahlik)
- [Tracking] Introduce autologging support for native PyTorch models (#7627, @temporaer)
- [Tracking] Allow specifying `model_format` when autologging XGBoost models (#7781, @guyrosin)
- [Tracking] Add `MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT` environment variable to configure artifact operation timeouts (#7783, @wamartin-aml)
- [Artifacts] Include `Content-Type` response headers for artifacts downloaded from `mlflow server` (#7827, @bali0019)
- [Model Registry] Introduce the `searchModelVersions()` API to the Java client (#7880, @gabrielfu)
- [Model Registry] Introduce `max_results`, `order_by` and `page_token` arguments to `MlflowClient.search_model_versions()` (#7623, @serena-ruan)
- [Models] Support logging large ONNX models by using external data (#7808, @dogeplusplus)
- [Models] Add support for logging Diviner models fit in Spark (#7800, @BenWilson2)
- [Models] Introduce `MLFLOW_DEFAULT_PREDICTION_DEVICE` environment variable to set the device for pyfunc model inference (#7922, @ankit-db)
- [Scoring] Publish official Docker images for the MLflow Model scoring server at github.com/mlflow/mlflow/pkgs (#7759, @dbczumar)

Bug fixes:

- [Recipes] Fix dataset format validation in the ingest step for custom dataset sources (#7638, @sunishsheth2009)
- [Recipes] Fix bug in identification of worst performing examples during training (#7658, @sunishsheth2009)
- [Recipes] Ensure consistent rendering of the recipe graph when `inspect()` is called (#7852, @sunishsheth2009)
- [Recipes] Correctly respect `positive_class` configuration in the transform step (#7626, @sunishsheth2009)
- [Recipes] Make logged metric names consistent with `mlflow.evaluate()` (#7613, @sunishsheth2009)
- [Recipes] Add `run_id` and `artifact_path` keys to logged MLmodel files (#7651, @sunishsheth2009)
- [UI] Fix bugs in UI validation of experiment names, model names, and tag keys (#7818, @subramaniam02)
- [Tracking] Resolve artifact locations to absolute paths when creating experiments (#7670, @bali0019)
- [Tracking] Exclude Delta checkpoints from Spark datasource autologging (#7902, @harupy)
- [Tracking] Consistently return an empty list from GetMetricHistory when a metric does not exist (#7589, @bali0019; #7659, @harupy)
- [Artifacts] Fix support for artifact operations on Windows paths in UNC format (#7750, @bali0019)
- [Artifacts] Fix bug in HDFS artifact listing (#7581, @pwnywiz)
- [Model Registry] Disallow creation of model versions with local filesystem sources in `mlflow server` (#7908, @harupy)
- [Model Registry] Fix handling of deleted model versions in FileStore (#7716, @harupy)
- [Model Registry] Correctly initialize Model Registry SQL tables independently of MLflow Tracking (#7704, @harupy)
- [Models] Correctly move PyTorch model outputs from GPUs to CPUs during inference with pyfunc (#7885, @ankit-db)
- [Build] Fix compatiblility issues with Python installations compiled using `PYTHONOPTIMIZE=2` (#7791, @dbczumar)
- [Build] Fix compatibility issues with the upcoming pandas 2.0 release (#7899, @harupy; #7910, @dbczumar)

Documentation updates:

- [Docs] Add an example of saving and loading Spark MLlib models with MLflow (#7706, @dipanjank)
- [Docs] Add usage examples for `mlflow.lightgbm` APIs (#7565, @canerturkseven)
- [Docs] Add an example of custom model flavor creation with `sktime` (#7624, @benjaminbluhm)
- [Docs] Clarify `precision_recall_auc` metric calculation in `mlflow.evaluate()` (#7701, @BenWilson2)
- [Docs] Remove outdated example links (#7587, @asloan7)

Small bug fixes and documentation updates:

#7866, #7751, #7724, #7699, #7697, #7666, @alekseyolg; #7896, #7861, #7858, #7862, #7872, #7859, #7863, #7767, #7766, #7765, #7741, @smurching; #7895, #7877, @viditjain99; #7898, @midhun1998; #7891, #7892, #7886, #7882, #7883, #7875, #7874, #7871, #7868, #7854, #7847, #7845, #7838, #7830, #7837, #7836, #7834, #7831, #7828, #7825, #7826, #7824, #7823, #7778, #7780, #7776, #7775, #7773, #7772, #7769, #7756, #7768, #7764, #7685, #7726, #7722, #7720, #7423, #7712, #7710, #7713, #7688, #7663, #7674, #7673, #7672, #7662, #7653, #7646, #7615, #7614, #7586, #7601, #7598, #7602, #7599, #7577, #7585, #7583, #7584, @harupy; #7865, #7803, #7753, #7719, @dipanjank; #7796, @serena-ruan; #7849, @turbotimon; #7822, #7600, @WeichenXu123; #7811, @guyrosin; #7812, #7788, #7787, #7748, #7730, #7616, #7593, @dbczumar; #7793, @Joel-hanson; #7792, #7694, #7643, @BenWilson2; #7771, #7657, #7644, @nsenno-dbr; #7738, @wkrt7; #7740, @Ark-kun; #7739, #7733, @bali0019; #7723, @andrehp; #7691, #7582, @agoyot; #7721, @Eseeldur; #7709, @srowen; #7693, @ry3s; #7649, @funkypenguin; #7665, @benjaminbluhm; #7668, @eltociear; #7550, @danielhstahl; #7920, @arjundc-db

## 2.1.0 (2022-12-21)

MLflow 2.1.0 includes several major features and improvements

Features:

- [Recipes] Introduce support for multi-class classification (#7458, @mshtelma)
- [Recipes] Extend the pyfunc representation of classification models to output scores in addition to labels (#7474, @sunishsheth2009)
- [UI] Add user ID and lifecycle stage quick search links to the Runs page (#7462, @jaeday)
- [Tracking] Paginate the GetMetricHistory API (#7523, #7415, @BenWilson2)
- [Tracking] Add Runs search aliases for Run name and start time that correspond to UI column names (#7492, @apurva-koti)
- [Tracking] Add a `/version` endpoint to `mlflow server` for querying the server's MLflow version (#7273, @joncarter1)
- [Model Registry] Add FileStore support for the Model Registry (#6605, @serena-ruan)
- [Model Registry] Introduce an `mlflow.search_registered_models()` fluent API (#7428, @TSienki)
- [Model Registry / Java] Add a `getRegisteredModel()` method to the Java client (#6602) (#7511, @drod331)
- [Model Registry / R] Add an `mlflow_set_model_version_tag()` method to the R client (#7401, @leeweijie)
- [Models] Introduce a `metadata` field to the MLmodel specification and `log_model()` methods (#7237, @jdonzallaz)
- [Models] Extend `Model.load()` to support loading MLmodel specifications from remote locations (#7517, @dbczumar)
- [Models] Pin the major version of MLflow in Models' `requirements.txt` and `conda.yaml` files (#7364, @BenWilson2)
- [Scoring] Extend `mlflow.pyfunc.spark_udf()` to support StructType results (#7527, @WeichenXu123)
- [Scoring] Extend TensorFlow and Keras Models to support multi-dimensional inputs with `mlflow.pyfunc.spark_udf()`(#7531, #7291, @WeichenXu123)
- [Scoring] Support specifying deployment environment variables and tags when deploying models to SageMaker (#7433, @jhallard)

Bug fixes:

- [Recipes] Fix a bug that prevented use of custom `early_stop` functions during model tuning (#7538, @sunishsheth2009)
- [Recipes] Fix a bug in the logic used to create a Spark session during data ingestion (#7307, @WeichenXu123)
- [Tracking] Make the metric names produced by `mlflow.autolog()` consistent with `mlflow.evaluate()` (#7418, @wenfeiy-db)
- [Tracking] Fix an autologging bug that caused nested, redundant information to be logged for XGBoost and LightGBM models (#7404, @WeichenXu123)
- [Tracking] Correctly classify SQLAlchemy OperationalErrors as retryable HTTP errors (#7240, @barrywhart)
- [Artifacts] Correctly handle special characters in credentials when using FTP artifact storage (#7479, @HCTsai)
- [Models] Address an issue that prevented MLeap models from being saved on Windows (#6966, @dbczumar)
- [Scoring] Fix a permissions issue encountered when using NFS during model scoring with `mlflow.pyfunc.spark_udf()` (#7427, @WeichenXu123)

Documentation updates:

- [Docs] Add more examples to the Runs search documentation page (#7487, @apurva-koti)
- [Docs] Add documentation for Model flavors developed by the community (#7425, @mmerce)
- [Docs] Add an example for logging and scoring ONNX Models (#7398, @Rusteam)
- [Docs] Fix a typo in the model scoring REST API example for inputs with the `dataframe_split` format (#7540, @zhouyangyu)
- [Docs] Fix a typo in the model scoring REST API example for inputs with the `dataframe_records` format (#7361, @dbczumar)

Small bug fixes and documentation updates:

#7571, #7543, #7529, #7435, #7399, @WeichenXu123; #7568, @xiaoye-hua; #7549, #7557, #7509, #7498, #7499, #7485, #7486, #7484, #7391, #7388, #7390, #7381, #7366, #7348, #7346, #7334, #7340, #7323, @BenWilson2; #7561, #7562, #7560, #7553, #7546, #7539, #7544, #7542, #7541, #7533, #7507, #7470, #7469, #7467, #7466, #7464, #7453, #7449, #7450, #7440, #7430, #7436, #7429, #7426, #7410, #7406, #7409, #7407, #7405, #7396, #7393, #7395, #7384, #7376, #7379, #7375, #7354, #7353, #7351, #7352, #7350, #7345, #6493, #7343, #7344, @harupy; #7494, @dependabot[bot]; #7526, @tobycheese; #7489, @liangz1; #7534, @Jingnan-Jia; #7496, @danielhstahl; #7504, #7503, #7459, #7454, #7447, @tsugumi-sys; #7461, @wkrt7; #7451, #7414, #7372, #7289, @sunishsheth2009; #7441, @ikrizanic; #7432, @Pochingto; #7386, @jhallard; #7370, #7373, #7371, #7336, #7341, #7342, @dbczumar; #7335, @prithvikannan

## 2.0.1 (2022-11-14)

The 2.0.1 version of MLflow is a major milestone release that focuses on simplifying the management of end-to-end MLOps workflows, providing new feature-rich functionality, and expanding upon the production-ready MLOps capabilities offered by MLflow.
This release contains several important breaking changes from the 1.x API, additional major features and improvements.

Features:

- [Recipes] MLflow Pipelines is now MLflow Recipes - a framework that enables data scientists to quickly develop high-quality models and deploy them to production
- [Recipes] Add support for classification models to MLflow Recipes (#7082, @bbarnes52)
- [UI] Introduce support for pinning runs within the experiments UI (#7177, @harupy)
- [UI] Simplify the layout and provide customized displays of metrics, parameters, and tags within the experiments UI (#7177, @harupy)
- [UI] Simplify run filtering and ordering of runs within the experiments UI (#7177, @harupy)
- [Tracking] Update `mlflow.pyfunc.get_model_dependencies()` to download all referenced requirements files for specified models (#6733, @harupy)
- [Tracking] Add support for selecting the Keras model `save_format` used by `mlflow.tensorflow.autolog()` (#7123, @balvisio)
- [Models] Set `mlflow.evaluate()` status to stable as it is now a production-ready API
- [Models] Simplify APIs for specifying custom metrics and custom artifacts during model evaluation with `mlflow.evaluate()` (#7142, @harupy)
- [Models] Correctly infer the positive label for binary classification within `mlflow.evaluate()` (#7149, @dbczumar)
- [Models] Enable automated signature logging for `tensorflow` and `keras` models when `mlflow.tensorflow.autolog()` is enabled (#6678, @BenWilson2)
- [Models] Add support for native Keras and Tensorflow Core models within `mlflow.tensorflow` (#6530, @WeichenXu123)
- [Models] Add support for defining the `model_format` used by `mlflow.xgboost.save/log_model()` (#7068, @AvikantSrivastava)
- [Scoring] Overhaul the model scoring REST API to introduce format indicators for inputs and support multiple output fields (#6575, @tomasatdatabricks; #7254, @adriangonz)
- [Scoring] Add support for ragged arrays in model signatures (#7135, @trangevi)
- [Java] Add `getModelVersion` API to the java client (#6955, @wgottschalk)

Breaking Changes:

The following list of breaking changes are arranged by their order of significance within each category.

- [Core] Support for Python 3.7 has been dropped. MLflow now requires Python >=3.8
- [Recipes] `mlflow.pipelines` APIs have been replaced with `mlflow.recipes`
- [Tracking / Registry] Remove `/preview` routes for Tracking and Model Registry REST APIs (#6667, @harupy)
- [Tracking] Remove deprecated `list` APIs for experiments, models, and runs from Python, Java, R, and REST APIs (#6785, #6786, #6787, #6788, #6800, #6868, @dbczumar)
- [Tracking] Remove deprecated `runs` response field from `Get Experiment` REST API response (#6541, #6524 @dbczumar)
- [Tracking] Remove deprecated `MlflowClient.download_artifacts` API (#6537, @WeichenXu123)
- [Tracking] Change the behavior of environment variable handling for `MLFLOW_EXPERIMENT_NAME` such that the value is always used when creating an experiment (#6674, @BenWilson2)
- [Tracking] Update `mlflow server` to run in `--serve-artifacts` mode by default (#6502, @harupy)
- [Tracking] Update Experiment ID generation for the Filestore backend to enable threadsafe concurrency (#7070, @BenWilson2)
- [Tracking] Remove `dataset_name` and `on_data_{name | hash}` suffixes from `mlflow.evaluate()` metric keys (#7042, @harupy)
- [Models / Scoring / Projects] Change default environment manager to `virtualenv` instead of `conda` for model inference and project execution (#6459, #6489 @harupy)
- [Models] Move Keras model logging APIs to the `mlflow.tensorflow` flavor and drop support for TensorFlow Estimators (#6530, @WeichenXu123)
- [Models] Remove deprecated `mlflow.sklearn.eval_and_log_metrics()` API in favor of `mlflow.evaluate()` API (#6520, @dbczumar)
- [Models] Require `mlflow.evaluate()` model inputs to be specified as URIs (#6670, @harupy)
- [Models] Drop support for returning custom metrics and artifacts from the same function when using `mlflow.evaluate()`, in favor of `custom_artifacts` (#7142, @harupy)
- [Models] Extend `PyFuncModel` spec to support `conda` and `virtualenv` subfields (#6684, @harupy)
- [Scoring] Remove support for defining input formats using the `Content-Type` header (#6575, @tomasatdatabricks; #7254, @adriangonz)
- [Scoring] Replace the `--no-conda` CLI option argument for native serving with `--env-manager='local'` (#6501, @harupy)
- [Scoring] Remove public APIs for `mlflow.sagemaker.deploy()` and `mlflow.sagemaker.delete()` in favor of MLflow deployments APIs, such as `mlflow deployments -t sagemaker` (#6650, @dbczumar)
- [Scoring] Rename input argument `df` to `inputs` in `mlflow.deployments.predict()` method (#6681, @BenWilson2)
- [Projects] Replace the `use_conda` argument with the `env_manager` argument within the `run` CLI command for MLflow Projects (#6654, @harupy)
- [Projects] Modify the MLflow Projects docker image build options by renaming `--skip-image-build` to `--build-image` with a default of `False` (#7011, @harupy)
- [Integrations/Azure] Remove deprecated `mlflow.azureml` modules from MLflow in favor of the `azure-mlflow` deployment plugin (#6691, @BenWilson2)
- [R] Remove conda integration with the R client (#6638, @harupy)

Bug fixes:

- [Recipes] Fix rendering issue with profile cards polyfill (#7154, @hubertzub-db)
- [Tracking] Set the MLflow Run name correctly when specified as part of the `tags` argument to `mlflow.start_run()` (#7228, @Cokral)
- [Tracking] Fix an issue with conflicting MLflow Run name assignment if the `mlflow.runName` tag is set (#7138, @harupy)
- [Scoring] Fix incorrect payload constructor error in SageMaker deployment client `predict()` API (#7193, @dbczumar)
- [Scoring] Fix an issue where `DataCaptureConfig` information was not preserved when updating a Sagemaker deployment (#7281, @harupy)

Small bug fixes and documentation updates:

#7309, #7314, #7288, #7276, #7244, #7207, #7175, #7107, @sunishsheth2009; #7261, #7313, #7311, #7249, #7278, #7260, #7284, #7283, #7263, #7266, #7264, #7267, #7265, #7250, #7259, #7247, #7242, #7143, #7214, #7226, #7230, #7227, #7229, #7225, #7224, #7223, #7210, #7192, #7197, #7196, #7204, #7198, #7191, #7189, #7184, #7182, #7170, #7183, #7131, #7165, #7151, #7164, #7168, #7150, #7128, #7028, #7118, #7117, #7102, #7072, #7103, #7101, #7100, #7099, #7098, #7041, #7040, #6978, #6768, #6719, #6669, #6658, #6656, #6655, #6538, #6507, #6504 @harupy; #7310, #7308, #7300, #7290, #7239, #7220, #7127, #7091, #6713 @BenWilson2; #7332, #7299, #7271, #7209, #7180, #7179, #7158, #7147, #7114, @prithvikannan; #7275, #7245, #7134, #7059, @jinzhang21; #7306, #7298, #7287, #7272, #7258, #7236, @ayushthe1; #7279, @tk1012; #7219, @rddefauw; #7333, #7218, #7208, #7188, #7190, #7176, #7137, #7136, #7130, #7124, #7079, #7052, #6541 @dbczumar; #6640, @WeichenXu123; #7200, @hubertzub-db; #7121, @Gonmeso; #6988, @alonisser; #7141, @pdifranc; #7086, @jerrylian-db; #7286, @shogohida

## 1.30.0 (2022-10-19)

MLflow 1.30.0 includes several major features and improvements

Features:

- [Pipelines] Introduce hyperparameter tuning support to MLflow Pipelines (#6859, @prithvikannan)
- [Pipelines] Introduce support for prediction outlier comparison to training data set (#6991, @jinzhang21)
- [Pipelines] Introduce support for recording all training parameters for reproducibility (#7026, #7094, @prithvikannan)
- [Pipelines] Add support for `Delta` tables as a datasource in the ingest step (#7010, @sunishsheth2009)
- [Pipelines] Add expanded support for data profiling up to 10,000 columns (#7035, @prithvikanna)
- [Pipelines] Add support for AutoML in MLflow Pipelines using FLAML (#6959, @mshtelma)
- [Pipelines] Add support for simplified transform step execution by allowing for unspecified configuration (#6909, @apurva-koti)
- [Pipelines] Introduce a data preview tab to the transform step card (#7033, @prithvikannan)
- [Tracking] Introduce `run_name` attribute for `create_run`, `get_run` and `update_run` APIs (#6782, #6798 @apurva-koti)
- [Tracking] Add support for searching by `creation_time` and `last_update_time` for the `search_experiments` API (#6979, @harupy)
- [Tracking] Add support for search terms `run_id IN` and `run ID NOT IN` for the `search_runs` API (#6945, @harupy)
- [Tracking] Add support for searching by `user_id` and `end_time` for the `search_runs` API (#6881, #6880 @subramaniam02)
- [Tracking] Add support for searching by `run_name` and `run_id` for the `search_runs` API (#6899, @harupy; #6952, @alexacole)
- [Tracking] Add support for synchronizing run `name` attribute and `mlflow.runName` tag (#6971, @BenWilson2)
- [Tracking] Add support for signed tracking server requests using AWSSigv4 and AWS IAM (#7044, @pdifranc)
- [Tracking] Introduce the `update_run()` API for modifying the `status` and `name` attributes of existing runs (#7013, @gabrielfu)
- [Tracking] Add support for experiment deletion in the `mlflow gc` cli API (#6977, @shaikmoeed)
- [Models] Add support for environment restoration in the `evaluate()` API (#6728, @jerrylian-db)
- [Models] Remove restrictions on binary classification labels in the `evaluate()` API (#7077, @dbczumar)
- [Scoring] Add support for `BooleanType` to `mlflow.pyfunc.spark_udf()` (#6913, @BenWilson2)
- [SQLAlchemy] Add support for configurable `Pool` class options for `SqlAlchemyStore` (#6883, @mingyu89)

Bug fixes:

- [Pipelines] Enable Pipeline subprocess commands to create a new `SparkSession` if one does not exist (#6846, @prithvikannan)
- [Pipelines] Fix a rendering issue with `bool` column types in Step Card data profiles (#6907, @sunishsheth2009)
- [Pipelines] Add validation and an exception if required step files are missing (#7067, @mingyu89)
- [Pipelines] Change step configuration validation to only be performed during runtime execution of a step (#6967, @prithvikannan)
- [Tracking] Fix infinite recursion bug when inferring the model schema in `mlflow.pyspark.ml.autolog()` (#6831, @harupy)
- [UI] Remove the browser error notification when failing to fetch artifacts (#7001, @kevingreer)
- [Models] Allow `mlflow-skinny` package to serve as base requirement in `MLmodel` requirements (#6974, @BenWilson2)
- [Models] Fix an issue with code path resolution for loading SparkML models (#6968, @dbczumar)
- [Models] Fix an issue with dependency inference in logging SparkML models (#6912, @BenWilson2)
- [Models] Fix an issue involving potential duplicate downloads for SparkML models (#6903, @serena-ruan)
- [Models] Add missing `pos_label` to `sklearn.metrics.precision_recall_curve` in `mlflow.evaluate()` (#6854, @dbczumar)
- [SQLAlchemy] Fix a bug in `SqlAlchemyStore` where `set_tag()` updates the incorrect tags (#7027, @gabrielfu)

Documentation updates:

- [Models] Update details regarding the default `Keras` serialization format (#7022, @balvisio)

Small bug fixes and documentation updates:

#7093, #7095, #7092, #7064, #7049, #6921, #6920, #6940, #6926, #6923, #6862, @jerrylian-db; #6946, #6954, #6938, @mingyu89; #7047, #7087, #7056, #6936, #6925, #6892, #6860, #6828, @sunishsheth2009; #7061, #7058, #7098, #7071, #7073, #7057, #7038, #7029, #6918, #6993, #6944, #6976, #6960, #6933, #6943, #6941, #6900, #6901, #6898, #6890, #6888, #6886, #6887, #6885, #6884, #6849, #6835, #6834, @harupy; #7094, #7065, #7053, #7026, #7034, #7021, #7020, #6999, #6998, #6996, #6990, #6989, #6934, #6924, #6896, #6895, #6876, #6875, #6861, @prithvikannan; #7081, #7030, #7031, #6965, #6750, @bbarnes52; #7080, #7069, #7051, #7039, #7012, #7004, @dbczumar; #7054, @jinzhang21; #7055, #7037, #7036, #6949, #6951, @apurva-koti; #6815, @michaguenther; #6897, @chaturvedakash; #7025, #6981, #6950, #6948, #6937, #6829, #6830, @BenWilson2; #6982, @vadim; #6985, #6927, @kriscon-db; #6917, #6919, #6872, #6855, @WeichenXu123; #6980, @utkarsh867; #6973, #6935, @wentinghu; #6930, @mingyangge-db; #6956, @RohanBha1; #6916, @av-maslov; #6824, @shrinath-suresh; #6732, @oojo12; #6807, @ikrizanic; #7066, @subramaniam20jan; #7043, @AvikantSrivastava; #6879, @jspablo

## 1.29.0 (2022-09-16)

MLflow 1.29.0 includes several major features and improvements

Features:

- [Pipelines] Improve performance and fidelity of dataset profiling in the scikit-learn regression Pipeline (#6792, @sunishsheth2009)
- [Pipelines] Add an `mlflow pipelines get-artifact` CLI for retrieving Pipeline artifacts (#6517, @prithvikannan)
- [Pipelines] Introduce an option for skipping dataset profiling to the scikit-learn regression Pipeline (#6456, @apurva-koti)
- [Pipelines / UI] Display an `mlflow pipelines` CLI command for reproducing a Pipeline run in the MLflow UI (#6376, @hubertzub-db)
- [Tracking] Automatically generate friendly names for Runs if not supplied by the user (#6736, @BenWilson2)
- [Tracking] Add `load_text()`, `load_image()` and `load_dict()` fluent APIs for convenient artifact loading (#6475, @subramaniam02)
- [Tracking] Add `creation_time` and `last_update_time` attributes to the Experiment class (#6756, @subramaniam02)
- [Tracking] Add official MLflow Tracking Server Dockerfiles to the MLflow repository (#6731, @oojo12)
- [Tracking] Add `searchExperiments` API to Java client and deprecate `listExperiments` (#6561, @dbczumar)
- [Tracking] Add `mlflow_search_experiments` API to R client and deprecate `mlflow_list_experiments` (#6576, @dbczumar)
- [UI] Make URLs clickable in the MLflow Tracking UI (#6526, @marijncv)
- [UI] Introduce support for csv data preview within the artifact viewer pane (#6567, @nnethery)
- [Model Registry / Models] Introduce `mlflow.models.add_libraries_to_model()` API for adding libraries to an MLflow Model (#6586, @arjundc-db)
- [Models] Add model validation support to `mlflow.evaluate()` (#6582, @jerrylian-db)
- [Models] Introduce `sample_weights` support to `mlflow.evaluate()` (#6806, @dbczumar)
- [Models] Add `pos_label` support to `mlflow.evaluate()` for identifying the positive class (#6696, @harupy)
- [Models] Make the metric name prefix and dataset info configurable in `mlflow.evaluate()` (#6593, @dbczumar)
- [Models] Add utility for validating the compatibility of a dataset with a model signature (#6494, @serena-ruan)
- [Models] Add `predict_proba()` support to the pyfunc representation of scikit-learn models (#6631, @skylarbpayne)
- [Models] Add support for Decimal type inference to MLflow Model schemas (#6600, @shitaoli-db)
- [Models] Add new CLI command for generating Dockerfiles for model serving (#6591, @anuarkaliyev23)
- [Scoring] Add `/health` endpoint to scoring server (#6574, @gabriel-milan)
- [Scoring] Support specifying a `variant_name` during Sagemaker deployment (#6486, @nfarley-soaren)
- [Scoring] Support specifying a `data_capture_config` during SageMaker deployment (#6423, @jonwiggins)

Bug fixes:

- [Tracking] Make Run and Experiment deletion and restoration idempotent (#6641, @dbczumar)
- [UI] Fix an alignment bug affecting the Experiments list in the MLflow UI (#6569, @sunishsheth2009)
- [Models] Fix a regression in the directory path structure of logged Spark Models that occurred in MLflow 1.28.0 (#6683, @gwy1995)
- [Models] No longer reload the `__main__` module when loading model code (#6647, @Jooakim)
- [Artifacts] Fix an `mlflow server` compatibility issue with HDFS when running in `--serve-artifacts` mode (#6482, @shidianshifen)
- [Scoring] Fix an inference failure with 1-dimensional tensor inputs in TensorFlow and Keras (#6796, @LiamConnell)

Documentation updates:

- [Tracking] Mark the SearchExperiments API as stable (#6551, @dbczumar)
- [Tracking / Model Registry] Deprecate the ListExperiments, ListRegisteredModels, and `list_run_infos()` APIs (#6550, @dbczumar)
- [Scoring] Deprecate `mlflow.sagemaker.deploy()` in favor of `SageMakerDeploymentClient.create()` (#6651, @dbczumar)

Small bug fixes and documentation updates:

#6803, #6804, #6801, #6791, #6772, #6745, #6762, #6760, #6761, #6741, #6725, #6720, #6666, #6708, #6717, #6704, #6711, #6710, #6706, #6699, #6700, #6702, #6701, #6685, #6664, #6644, #6653, #6629, #6639, #6624, #6565, #6558, #6557, #6552, #6549, #6534, #6533, #6516, #6514, #6506, #6509, #6505, #6492, #6490, #6478, #6481, #6464, #6463, #6460, #6461, @harupy; #6810, #6809, #6727, #6648, @BenWilson2; #6808, #6766, #6729, @jerrylian-db; #6781, #6694, @marijncv; #6580, #6661, @bbarnes52; #6778, #6687, #6623, @shraddhafalane; #6662, #6737, #6612, #6595, @sunishsheth2009; #6777, @aviralsharma07; #6665, #6743, #6573, @liangz1; #6784, @apurva-koti; #6753, #6751, @mingyu89; #6690, #6455, #6484, @kriscon-db; #6465, #6689, @hubertzub-db; #6721, @WeichenXu123; #6722, #6718, #6668, #6663, #6621, #6547, #6508, #6474, #6452, @dbczumar; #6555, #6584, #6543, #6542, #6521, @dsgibbons; #6634, #6596, #6563, #6495, @prithvikannan; #6571, @smurching; #6630, #6483, @serena-ruan; #6642, @thinkall; #6614, #6597, @jinzhang21; #6457, @cnphil; #6570, #6559, @kumaryogesh17; #6560, #6540, @iamthen0ise; #6544, @Monkero; #6438, @ahlag; #3292, @dolfinus; #6637, @ninabacc-db; #6632, @arpitjasa-db

## 1.28.0 (2022-08-09)

MLflow 1.28.0 includes several major features and improvements:

Features:

- [Pipelines] Log the full Pipeline runtime configuration to MLflow Tracking during Pipeline execution (#6359, @jinzhang21)
- [Pipelines] Add `pipeline.yaml` configurations to specify the Model Registry backend used for model registration (#6284, @sunishsheth2009)
- [Pipelines] Support optionally skipping the `transform` step of the scikit-learn regression pipeline (#6362, @sunishsheth2009)
- [Pipelines] Add UI links to Runs and Models in Pipeline Step Cards on Databricks (#6294, @dbczumar)
- [Tracking] Introduce `mlflow.search_experiments()` API for searching experiments by name and by tags (#6333, @WeichenXu123; #6227, #6172, #6154, @harupy)
- [Tracking] Increase the maximum parameter value length supported by File and SQL backends to 500 characters (#6358, @johnyNJ)
- [Tracking] Introduce an `--older-than` flag to `mlflow gc` for removing runs based on deletion time (#6354, @Jason-CKY)
- [Tracking] Add `MLFLOW_SQLALCHEMYSTORE_POOL_RECYCLE` environment variable for recycling SQLAlchemy connections (#6344, @postrational)
- [UI] Display deeply nested runs in the Runs Table on the Experiment Page (#6065, @tospe)
- [UI] Add box plot visualization for metrics to the Compare Runs page (#6308, @ahlag)
- [UI] Display tags on the Compare Runs page (#6164, @CaioCavalcanti)
- [UI] Use scientific notation for axes when viewing metric plots in log scale (#6176, @RajezMariner)
- [UI] Add button to Metrics page for downloading metrics as CSV (#6048, @rafaelvp-db)
- [UI] Include NaN and +/- infinity values in plots on the Metrics page (#6422, @hubertzub-db)
- [Tracking / Model Registry] Introduce environment variables to control retry behavior and timeouts for REST API requests (#5745, @peterdhansen)
- [Tracking / Model Registry] Make `MlflowClient` importable as `mlflow.MlflowClient` (#6085, @subramaniam02)
- [Model Registry] Add support for searching registered models and model versions by tags (#6413, #6411, #6320, @WeichenXu123)
- [Model Registry] Add `stage` parameter to `set_model_version_tag()` (#6185, @subramaniam02)
- [Model Registry] Add `--registry-store-uri` flag to `mlflow server` for specifying the Model Registry backend URI (#6142, @Secbone)
- [Models] Improve performance of Spark Model logging on Databricks (#6282, @bbarnes52)
- [Models] Include Pandas Series names in inferred model schemas (#6361, @RynoXLI)
- [Scoring] Make `model_uri` optional in `mlflow models build-docker` to support building generic model serving images (#6302, @harupy)
- [R] Support logging of NA and NaN parameter values (#6263, @nathaneastwood)

Bug fixes and documentation updates:

- [Pipelines] Improve scikit-learn regression pipeline latency by limiting dataset profiling to the first 100 columns (#6297, @sunishsheth2009)
- [Pipelines] Use `xdg-open` instead of `open` for viewing Pipeline results on Linux systems (#6326, @strangiato)
- [Pipelines] Fix a bug that skipped Step Card rendering in Jupyter Notebooks (#6378, @apurva-koti)
- [Tracking] Use the 401 HTTP response code in authorization failure REST API responses, instead of 500 (#6106, @balvisio)
- [Tracking] Correctly classify artifacts as files and directories when using Azure Blob Storage (#6237, @nerdinand)
- [Tracking] Fix a bug in the File backend that caused run metadata to be lost in the event of a failed write (#6388, @dbczumar)
- [Tracking] Adjust `mlflow.pyspark.ml.autolog()` to only log model signatures for supported input / output data types (#6365, @harupy)
- [Tracking] Adjust `mlflow.tensorflow.autolog()` to log TensorFlow early stopping callback info when `log_models=False` is specified (#6170, @WeichenXu123)
- [Tracking] Fix signature and input example logging errors in `mlflow.sklearn.autolog()` for models containing transformers (#6230, @dbczumar)
- [Tracking] Fix a failure in `mlflow gc` that occurred when removing a run whose artifacts had been previously deleted (#6165, @dbczumar)
- [Tracking] Add missing `sqlparse` library to MLflow Skinny client, which is required for search support (#6174, @dbczumar)
- [Tracking / Model Registry] Fix an `mlflow server` bug that rejected parameters and tags with empty string values (#6179, @dbczumar)
- [Model Registry] Fix a failure preventing model version schemas from being downloaded with `--serve-arifacts` enabled (#6355, @abbas123456)
- [Scoring] Patch the Java Model Server to support MLflow Models logged on recent versions of the Databricks Runtime (#6337, @dbczumar)
- [Scoring] Verify that either the deployment name or endpoint is specified when invoking the `mlflow deployments predict` CLI (#6323, @dbczumar)
- [Scoring] Properly encode datetime columns when performing batch inference with `mlflow.pyfunc.spark_udf()` (#6244, @harupy)
- [Projects] Fix an issue where local directory paths were misclassified as Git URIs when running Projects (#6218, @ElefHead)
- [R] Fix metric logging behavior for +/- infinity values (#6271, @nathaneastwood)
- [Docs] Move Python API docs for `MlflowClient` from `mlflow.tracking` to `mlflow.client` (#6405, @dbczumar)
- [Docs] Document that MLflow Pipelines requires Make (#6216, @dbczumar)
- [Docs] Improve documentation for developing and testing MLflow JS changes in `CONTRIBUTING.rst` (#6330, @ahlag)

Small bug fixes and doc updates (#6322, #6321, #6213, @KarthikKothareddy; #6409, #6408, #6396, #6402, #6399, #6398, #6397, #6390, #6381, #6386, #6385, #6373, #6375, #6380, #6374, #6372, #6363, #6353, #6352, #6350, #6351, #6349, #6347, #6287, #6341, #6342, #6340, #6338, #6319, #6314, #6316, #6317, #6318, #6315, #6313, #6311, #6300, #6292, #6291, #6289, #6290, #6278, #6279, #6276, #6272, #6252, #6243, #6250, #6242, #6241, #6240, #6224, #6220, #6208, #6219, #6207, #6171, #6206, #6199, #6196, #6191, #6190, #6175, #6167, #6161, #6160, #6153, @harupy; #6193, @jwgwalton; #6304, #6239, #6234, #6229, @sunishsheth2009; #6258, @xanderwebs; #6106, @balvisio; #6303, @bbarnes52; #6117, @wenfeiy-db; #6389, #6214, @apurva-koti; #6412, #6420, #6277, #6266, #6260, #6148, @WeichenXu123; #6120, @ameya-parab; #6281, @nathaneastwood; #6426, #6415, #6417, #6418, #6257, #6182, #6157, @dbczumar; #6189, @shrinath-suresh; #6309, @SamirPS; #5897, @temporaer; #6251, @herrmann; #6198, @sniafas; #6368, #6158, @jinzhang21; #6236, @subramaniam02; #6036, @serena-ruan; #6430, @ninabacc-db)

## 1.27.0 (2022-06-27)

MLflow 1.27.0 includes several major features and improvements:

- [**Pipelines**] With MLflow 1.27.0, we are excited to announce the release of
  [**MLflow Pipelines**](https://mlflow.org/docs/latest/pipelines.html), an opinionated framework for
  structuring MLOps workflows that simplifies and standardizes machine learning application development
  and productionization. MLflow Pipelines makes it easy for data scientists to follow best practices
  for creating production-ready ML deliverables, allowing them to focus on developing excellent models.
  MLflow Pipelines also enables ML engineers and DevOps teams to seamlessly deploy models to production
  and incorporate them into applications. To get started with MLflow Pipelines, check out the docs at
  https://mlflow.org/docs/latest/pipelines.html. (#6115)

- [UI] Introduce UI support for searching and comparing runs across multiple Experiments (#5971, @r3stl355)

More features:

- [Tracking] When using batch logging APIs, automatically split large sets of metrics, tags, and params into multiple requests (#6052, @nzw0301)
- [Tracking] When an Experiment is deleted, SQL-based backends also move the associate Runs to the "deleted" lifecycle stage (#6064, @AdityaIyengar27)
- [Tracking] Add support for logging single-element `ndarray` and tensor instances as metrics via the `mlflow.log_metric()` API (#5756, @ntakouris)
- [Models] Add support for `CatBoostRanker` models to the `mlflow.catboost` flavor (#6032, @danielgafni)
- [Models] Integrate SHAP's `KernelExplainer` with `mlflow.evaluate()`, enabling model explanations on categorical data (#6044, #5920, @WeichenXu123)
- [Models] Extend `mlflow.evaluate()` to automatically log the `score()` outputs of scikit-learn models as metrics (#5935, #5903, @WeichenXu123)

Bug fixes and documentation updates:

- [UI] Fix broken model links in the Runs table on the MLflow Experiment Page (#6014, @hctpbl)
- [Tracking/Installation] Require `sqlalchemy>=1.4.0` upon MLflow installation, which is necessary for usage of SQL-based MLflow Tracking backends (#6024, @sniafas)
- [Tracking] Fix a regression that caused `mlflow server` to reject `LogParam` API requests containing empty string values (#6031, @harupy)
- [Tracking] Fix a failure in scikit-learn autologging that occurred when `matplotlib` was not installed on the host system (#5995, @fa9r)
- [Tracking] Fix a failure in TensorFlow autologging that occurred when training models on `tf.data.Dataset` inputs (#6061, @dbczumar)
- [Artifacts] Address artifact download failures from SFTP locations that occurred due to mismanaged concurrency (#5840, @rsundqvist)
- [Models] Fix a bug where MLflow Models did not restore bundled code properly if multiple models use the same code module name (#5926, @BFAnas)
- [Models] Address an issue where `mlflow.sklearn.model()` did not properly restore bundled model code (#6037, @WeichenXu123)
- [Models] Fix a bug in `mlflow.evaluate()` that caused input data objects to be mutated when evaluating certain scikit-learn models (#6141, @dbczumar)
- [Models] Fix a failure in `mlflow.pyfunc.spark_udf` that occurred when the UDF was invoked on an empty RDD partition (#6063, @WeichenXu123)
- [Models] Fix a failure in `mlflow models build-docker` that occurred when `env-manager=local` was specified (#6046, @bneijt)
- [Projects] Improve robustness of the git repository check that occurs prior to MLflow Project execution (#6000, @dkapur17)
- [Projects] Address a failure that arose when running a Project that does not have a `master` branch (#5889, @harupy)
- [Docs] Correct several typos throughout the MLflow docs (#5959, @ryanrussell)

Small bug fixes and doc updates (#6041, @drsantos89; #6138, #6137, #6132, @sunishsheth2009; #6144, #6124, #6125, #6123, #6057, #6060, #6050, #6038, #6029, #6030, #6025, #6018, #6019, #5962, #5974, #5972, #5957, #5947, #5907, #5938, #5906, #5932, #5919, #5914, #5888, #5890, #5886, #5873, #5865, #5843, @harupy; #6113, @comojin1994; #5930, @yashaswikakumanu; #5837, @shrinath-suresh; #6067, @deepyaman; #5997, @idlefella; #6021, @BenWilson2; #5984, @Sumanth077; #5929, @krunal16-c; #5879, @kugland; #5875, @ognis1205; #6006, @ryanrussell; #6140, @jinzhang21; #5983, @elk15; #6022, @apurva-koti; #5982, @EB-Joel; #5981, #5980, @punitkashyup; #6103, @ikrizanic; #5988, #5969, @SaumyaBhushan; #6020, #5991, @WeichenXu123; #5910, #5912, @Dark-Knight11; #6005, @Asinsa; #6023, @subramaniam02; #5999, @Regis-Caelum; #6007, @CaioCavalcanti; #5943, @kvaithin; #6017, #6002, @NeoKish; #6111, @T1b4lt; #5986, @seyyidibrahimgulec; #6053, @Zohair-coder; #6146, #6145, #6143, #6139, #6134, #6136, #6135, #6133, #6071, #6070, @dbczumar; #6026, @rotate2050)

## 1.26.1 (2022-05-27)

MLflow 1.26.1 is a patch release containing the following bug fixes:

- [Installation] Fix compatibility issue with `protobuf >= 4.21.0` (#5945, @harupy)
- [Models] Fix `get_model_dependencies` behavior for `models:` URIs containing artifact paths (#5921, @harupy)
- [Models] Revert a problematic change to `artifacts` persistence in `mlflow.pyfunc.log_model()` that was introduced in MLflow 1.25.0 (#5891, @kyle-jarvis)
- [Models] Close associated image files when `EvaluationArtifact` outputs from `mlflow.evaluate()` are garbage collected (#5900, @WeichenXu123)

Small bug fixes and updates (#5874, #5942, #5941, #5940, #5938, @harupy; #5893, @PrajwalBorkar; #5909, @yashaswikakumanu; #5937, @BenWilson2)

## 1.26.0 (2022-05-16)

MLflow 1.26.0 includes several major features and improvements:

Features:

- [CLI] Add endpoint naming and options configuration to the deployment CLI (#5731, @trangevi)
- [Build,Doc] Add development environment setup script for Linux and MacOS x86 Operating Systems (#5717, @BenWilson2)
- [Tracking] Update `mlflow.set_tracking_uri` to add support for paths defined as `pathlib.Path` in addition to existing `str` path declarations (#5824, @cacharle)
- [Scoring] Add custom timeout override option to the scoring server CLI to support high latency models (#5663, @sniafas)
- [UI] Add sticky header to experiment run list table to support column name visibility when scrolling beyond page fold (#5818, @hubertzub-db)
- [Artifacts] Add GCS support for MLflow garbage collection (#5811, @aditya-iyengar-rtl-de)
- [Evaluate] Add `pos_label` argument for `eval_and_log_metrics` API to support accurate binary classifier evaluation metrics (#5807, @yxiong)
- [UI] Add fields for latest, minimum and maximum metric values on metric display page (#5574, @adamreeve)
- [Models] Add support for `input_example` and `signature` logging for pyspark ml flavor when using autologging (#5719, @bali0019)
- [Models] Add `virtualenv` environment manager support for `mlflow models docker-build` CLI (#5728, @harupy)
- [Models] Add support for wildcard module matching in log_model_allowlist for PySpark models (#5723, @serena-ruan)
- [Projects] Add `virtualenv` environment manager support for MLflow projects (#5631, @harupy)
- [Models] Add `virtualenv` environment manager support for MLflow Models (#5380, @harupy)
- [Models] Add `virtualenv` environment manager support for `mlflow.pyfunc.spark_udf` (#5676, @WeichenXu123)
- [Models] Add support for `input_example` and `signature` logging for `tensorflow` flavor when using autologging (#5510, @bali0019)
- [Server-infra] Add JSON Schema Type Validation to enable raising 400 errors on malformed requests to REST API endpoints (#5458, @mrkaye97)
- [Scoring] Introduce abstract `endpoint` interface for mlflow deployments (#5378, @trangevi)
- [UI] Add `End Time` and `Duration` fields to run comparison page (#3378, @RealArpanBhattacharya)
- [Serving] Add schema validation support when parsing input csv data for model serving (#5531, @vvijay-bolt)

Bug fixes and documentation updates:

- [Models] Fix REPL ID propagation from datasource listener to publisher for Spark data sources (#5826, @dbczumar)
- [UI] Update `ag-grid` and implement `getRowId` to improve performance in the runs table visualization (#5725, @adamreeve)
- [Serving] Fix `tf-serving` parsing to support columnar-based formatting (#5825, @arjundc-db)
- [Artifacts] Update `log_artifact` to support models larger than 2GB in HDFS (#5812, @hitchhicker)
- [Models] Fix autologging to support `lightgbm` metric names with "@" symbols within their names (#5785, @mengchendd)
- [Models] Pyfunc: Fix code directory resolution of subdirectories (#5806, @dbczumar)
- [Server-Infra] Fix mlflow-R server starting failure on windows (#5767, @serena-ruan)
- [Docs] Add documentation for `virtualenv` environment manager support for MLflow projects (#5727, @harupy)
- [UI] Fix artifacts display sizing to support full width rendering in preview pane (#5606, @szczeles)
- [Models] Fix local hostname issues when loading spark model by binding driver address to localhost (#5753, @WeichenXu123)
- [Models] Fix autologging validation and batch_size calculations for `tensorflow` flavor (#5683, @MarkYHZhang)
- [Artifacts] Fix `SqlAlchemyStore.log_batch` implementation to make it log data in batches (#5460, @erensahin)

Small bug fixes and doc updates (#5858, #5859, #5853, #5854, #5845, #5829, #5842, #5834, #5795, #5777, #5794, #5766, #5778, #5765, #5763, #5768, #5769, #5760, #5727, #5748, #5726, #5721, #5711, #5710, #5708, #5703, #5702, #5696, #5695, #5669, #5670, #5668, #5661, #5638, @harupy; #5749, @arpitjasa-db; #5675, @Davidswinkels; #5803, #5797, @ahlag; #5743, @kzhang01; #5650, #5805, #5724, #5720, #5662, @BenWilson2; #5627, @cterrelljones; #5646, @kutal10; #5758, @davideli-db; #5810, @rahulporuri; #5816, #5764, @shrinath-suresh; #5869, #5715, #5737, #5752, #5677, #5636, @WeichenXu123; #5735, @subramaniam02; #5746, @akaigraham; #5734, #5685, @lucalves; #5761, @marcelatoffernet; #5707, @aashish-khub; #5808, @ketangangal; #5730, #5700, @shaikmoeed; #5775, @dbczumar; #5747, @zhixuanevelynwu)

## 1.25.1 (2022-04-13)

MLflow 1.25.1 is a patch release containing the following bug fixes:

- [Models] Fix a `pyfunc` artifact overwrite bug for when multiple artifacts are saved in sub-directories (#5657, @kyle-jarvis)
- [Scoring] Fix permissions issue for Spark workers accessing model artifacts from a temp directory created by the driver (#5684, @WeichenXu123)

## 1.25.0 (2022-04-11)

MLflow 1.25.0 includes several major features and improvements:

Features:

- [Tracking] Introduce a new fluent API `mlflow.last_active_run()` that provides the most recent fluent active run (#5584, @MarkYHZhang)
- [Tracking] Add `experiment_names` argument to the `mlflow.search_runs()` API to support searching runs by experiment names (#5564, @r3stl355)
- [Tracking] Add a `description` parameter to `mlflow.start_run()` (#5534, @dogeplusplus)
- [Tracking] Add `log_every_n_step` parameter to `mlflow.pytorch.autolog()` to control metric logging frequency (#5516, @adamreeve)
- [Tracking] Log `pyspark.ml.param.Params` values as MLflow parameters during PySpark autologging (#5481, @serena-ruan)
- [Tracking] Add support for `pyspark.ml.Transformer`s to PySpark autologging (#5466, @serena-ruan)
- [Tracking] Add input example and signature autologging for Keras models (#5461, @bali0019)
- [Models] Introduce `mlflow.diviner` flavor for large-scale [time series forecasting](https://databricks-diviner.readthedocs.io/en/latest/?badge=latest) (#5553, @BenWilson2)
- [Models] Add `pyfunc.get_model_dependencies()` API to retrieve reproducible environment specifications for MLflow Models with the pyfunc flavor (#5503, @WeichenXu123)
- [Models] Add `code_paths` argument to all model flavors to support packaging custom module code with MLflow Models (#5448, @stevenchen-db)
- [Models] Support creating custom artifacts when evaluating models with `mlflow.evaluate()` (#5405, #5476 @MarkYHZhang)
- [Models] Add `mlflow_version` field to MLModel specification (#5515, #5576, @r3stl355)
- [Models] Add support for logging models to preexisting destination directories (#5572, @akshaya-a)
- [Scoring / Projects] Introduce `--env-manager` configuration for specifying environment restoration tools (e.g. `conda`) and deprecate `--no-conda` (#5567, @harupy)
- [Scoring] Support restoring model dependencies in `mlflow.pyfunc.spark_udf()` to ensure accurate predictions (#5487, #5561, @WeichenXu123)
- [Scoring] Add support for `numpy.ndarray` type inputs to the TensorFlow pyfunc `predict()` function (#5545, @WeichenXu123)
- [Scoring] Support deployment of MLflow Models to Sagemaker Serverless (#5610, @matthewmayo)
- [UI] Add MLflow version to header beneath logo (#5504, @adamreeve)
- [Artifacts] Introduce a `mlflow.artifacts.download_artifacts()` API mirroring the functionality of the `mlflow artifacts download` CLI (#5585, @dbczumar)
- [Artifacts] Introduce environment variables for controlling GCS artifact upload/download chunk size and timeouts (#5438, #5483, @mokrueger)

Bug fixes and documentation updates:

- [Tracking/SQLAlchemy] Create an index on `run_uuid` for PostgreSQL to improve query performance (#5446, @harupy)
- [Tracking] Remove client-side validation of metric, param, tag, and experiment fields (#5593, @BenWilson2)
- [Projects] Support setting the name of the MLflow Run when executing an MLflow Project (#5187, @bramrodenburg)
- [Scoring] Use pandas `split` orientation for DataFrame inputs to SageMaker deployment `predict()` API to preserve column ordering (#5522, @dbczumar)
- [Server-Infra] Fix runs search compatibility bugs with PostgreSQL, MySQL, and MSSQL (#5540, @harupy)
- [CLI] Fix a bug in the `mlflow-skinny` client that caused `mlflow --version` to fail (#5573, @BenWilson2)
- [Docs] Update guidance and examples for model deployment to AzureML to recommend using the `mlflow-azureml` package (#5491, @santiagxf)

Small bug fixes and doc updates (#5591, #5629, #5597, #5592, #5562, #5477, @BenWilson2; #5554, @juntai-zheng; #5570, @tahesse; #5605, @guelate; #5633, #5632, #5625, #5623, #5615, #5608, #5600, #5603, #5602, #5596, #5587, #5586, #5580, #5577, #5568, #5290, #5556, #5560, #5557, #5548, #5547, #5538, #5513, #5505, #5464, #5495, #5488, #5485, #5468, #5455, #5453, #5454, #5452, #5445, #5431, @harupy; #5640, @nchittela; #5520, #5422, @Ark-kun; #5639, #5604, @nishipy; #5543, #5532, #5447, #5435, @WeichenXu123; #5502, @singankit; #5500, @Sohamkayal4103; #5449, #5442, @apurva-koti; #5552, @vinijaiswal; #5511, @adamreeve; #5428, @jinzhang21; #5309, @sunishsheth2009; #5581, #5559, @Kr4is; #5626, #5618, #5529, @sisp; #5652, #5624, #5622, #5613, #5509, #5459, #5437, @dbczumar; #5616, @liangz1)

## 1.24.0 (2022-02-27)

MLflow 1.24.0 includes several major features and improvements:

Features:

- [Tracking] Support uploading, downloading, and listing artifacts through the MLflow server via `mlflow server --serve-artifacts` (#5320, @BenWilson2, @harupy)
- [Tracking] Add the `registered_model_name` argument to `mlflow.autolog()` for automatic model registration during autologging (#5395, @WeichenXu123)
- [UI] Improve and restructure the Compare Runs page. Additions include "show diff only" toggles and scrollable tables (#5306, @WeichenXu123)
- [Models] Introduce `mlflow.pmdarima` flavor for pmdarima models (#5373, @BenWilson2)
- [Models] When loading an MLflow Model, print a warning if a mismatch is detected between the current environment and the Model's dependencies (#5368, @WeichenXu123)
- [Models] Support computing custom scalar metrics during model evaluation with `mlflow.evaluate()` (#5389, @MarkYHZhang)
- [Scoring] Add support for deploying and evaluating SageMaker models via the [`MLflow Deployments API`](https://mlflow.org/docs/latest/models.html#deployment-to-custom-targets) (#4971, #5396, @jamestran201)

Bug fixes and documentation updates:

- [Tracking / UI] Fix artifact listing and download failures that occurred when operating the MLflow server in `--serve-artifacts` mode (#5409, @dbczumar)
- [Tracking] Support environment-variable-based authentication when making artifact requests to the MLflow server in `--serve-artifacts` mode (#5370, @TimNooren)
- [Tracking] Fix bugs in hostname and path resolution when making artifacts requests to the MLflow server in `--serve-artifacts` mode (#5384, #5385, @mert-kirpici)
- [Tracking] Fix an import error that occurred when `mlflow.log_figure()` was used without `matplotlib.figure` imported (#5406, @WeichenXu123)
- [Tracking] Correctly log XGBoost metrics containing the `@` symbol during autologging (#5403, @maxfriedrich)
- [Tracking] Fix a SQL Server database error that occurred during Runs search (#5382, @dianacarvalho1)
- [Tracking] When downloading artifacts from HDFS, store them in the user-specified destination directory (#5210, @DimaClaudiu)
- [Tracking / Model Registry] Improve performance of large artifact and model downloads (#5359, @mehtayogita)
- [Models] Fix fast.ai PyFunc inference behavior for models with 2D outputs (#5411, @santiagxf)
- [Models] Record Spark model information to the active run when `mlflow.spark.log_model()` is called (#5355, @szczeles)
- [Models] Restore onnxruntime execution providers when loading ONNX models with `mlflow.pyfunc.load_model()` (#5317, @ecm200)
- [Projects] Increase Docker image push timeout when using Projects with Docker (#5363, @zanitete)
- [Python] Fix a bug that prevented users from enabling DEBUG-level Python log outputs (#5362, @dbczumar)
- [Docs] Add a developer guide explaining how to build custom plugins for `mlflow.evaluate()` (#5333, @WeichenXu123)

Small bug fixes and doc updates (#5298, @wamartin-aml; #5399, #5321, #5313, #5307, #5305, #5268, #5284, @harupy; #5329, @Ark-kun; #5375, #5346, #5304, @dbczumar; #5401, #5366, #5345, @BenWilson2; #5326, #5315, @WeichenXu123; #5236, @singankit; #5302, @timvink; #5357, @maitre-matt; #5347, #5344, @mehtayogita; #5367, @apurva-koti; #5348, #5328, #5310, @liangz1; #5267, @sunishsheth2009)

## 1.23.1 (2022-01-27)

MLflow 1.23.1 is a patch release containing the following bug fixes:

- [Models] Fix a directory creation failure when loading PySpark ML models (#5299, @arjundc-db)
- [Model Registry] Revert to using case-insensitive validation logic for stage names in `models:/` URIs (#5312, @lichenran1234)
- [Projects] Fix a race condition during Project tar file creation (#5303, @dbczumar)

## 1.23.0 (2022-01-17)

MLflow 1.23.0 includes several major features and improvements:

Features:

- [Models] Introduce an `mlflow.evaluate()` API for evaluating MLflow Models, providing performance and explainability insights. For an overview, see https://mlflow.org/docs/latest/models.html#model-evaluation (#5069, #5092, #5256, @WeichenXu123)
- [Models] `log_model()` APIs now return information about the logged MLflow Model, including artifact location, flavors, and schema (#5230, @liangz1)
- [Models] Introduce an `mlflow.models.Model.load_input_example()` Python API for loading MLflow Model input examples (#5212, @maitre-matt)
- [Models] Add a UUID field to the MLflow Model specification. MLflow Models now have a unique identifier (#5149, #5167, @WeichenXu123)
- [Models] Support passing SciPy CSC and CSR matrices as MLflow Model input examples (#5016, @WeichenXu123)
- [Model Registry] Support specifying `latest` in model URI to get the latest version of a model regardless of the stage (#5027, @lichenran1234)
- [Tracking] Add support for LightGBM scikit-learn models to `mlflow.lightgbm.autolog()` (#5130, #5200, #5271 @jwyyy)
- [Tracking] Improve S3 artifact download speed by caching boto clients (#4695, @Samreay)
- [UI] Automatically update metric plots for in-progress runs (#5017, @cedkoffeto, @harupy)

Bug fixes and documentation updates:

- [Models] Fix a bug in MLflow Model schema enforcement where strings were incorrectly cast to Pandas objects (#5134, @stevenchen-db)
- [Models] Fix a bug where keyword arguments passed to `mlflow.pytorch.load_model()` were not applied for scripted models (#5163, @schmidt-jake)
- [Model Registry/R] Fix bug in R client `mlflow_create_model_version()` API that caused model `source` to be set incorrectly (#5185, @bramrodenburg)
- [Projects] Fix parsing behavior for Project URIs containing quotes (#5117, @dinaldoap)
- [Scoring] Use the correct 400-level error code for malformed MLflow Model Server requests (#5003, @abatomunkuev)
- [Tracking] Fix a bug where `mlflow.start_run()` modified user-supplied tags dictionary (#5191, @matheusMoreno)
- [UI] Fix a bug causing redundant scroll bars to be displayed on the Experiment Page (#5159, @sunishsheth2009)

Small bug fixes and doc updates (#5275, #5264, #5244, #5249, #5255, #5248, #5243, #5240, #5239, #5232, #5234, #5235, #5082, #5220, #5219, #5226, #5217, #5194, #5188, #5132, #5182, #5183, #5180, #5177, #5165, #5164, #5162, #5015, #5136, #5065, #5125, #5106, #5127, #5120, @harupy; #5045, @BenWilson2; #5156, @pbezglasny; #5202, @jwyyy; #3863, @JoshuaAnickat; #5205, @abhiramr; #4604, @OSobky; #4256, @einsmein; #5140, @AveshCSingh; #5273, #5186, #5176, @WeichenXu123; #5260, #5229, #5206, #5174, #5160, @liangz1)

## 1.22.0 (2021-11-29)

MLflow 1.22.0 includes several major features and improvements:

Features:

- [UI] Add a share button to the Experiment page (#4936, @marijncv)
- [UI] Improve readability of column sorting dropdown on Experiment page (#5022, @WeichenXu123; #5018, @NieuweNils, @coder-freestyle)
- [Tracking] Mark all autologging integrations as stable by removing `@experimental` decorators (#5028, @liangz1)
- [Tracking] Add optional `experiment_id` parameter to `mlflow.set_experiment()` (#5012, @dbczumar)
- [Tracking] Add support for XGBoost scikit-learn models to `mlflow.xgboost.autolog()` (#5078, @jwyyy)
- [Tracking] Improve statsmodels autologging performance by removing unnecessary metrics (#4942, @WeichenXu123)
- [Tracking] Update R client to tag nested runs with parent run ID (#4197, @yitao-li)
- [Models] Support saving and loading all XGBoost model types (#4954, @jwyyy)
- [Scoring] Support specifying AWS account and role when deploying models to SageMaker (#4923, @andresionek91)
- [Scoring] Support serving MLflow models with MLServer (#4963, @adriangonz)

Bug fixes and documentation updates:

- [UI] Fix bug causing Metric Plot page to crash when metric values are too large (#4947, @ianshan0915)
- [UI] Fix bug causing parallel coordinate curves to vanish (#5087, @harupy)
- [UI] Remove `Creator` field from Model Version page if user information is absent (#5089, @jinzhang21)
- [UI] Fix model loading instructions for non-pyfunc models in Artifact Viewer (#5006, @harupy)
- [Models] Fix a bug that added `mlflow` to `conda.yaml` even if a hashed version was already present (#5058, @maitre-matt)
- [Docs] Add Python documentation for metric, parameter, and tag key / value length limits (#4991, @westford14)
- [Examples] Update Python version used in Prophet example to fix installation errors (#5101, @BenWilson2)
- [Examples] Fix Kubernetes `resources` specification in MLflow Projects + Kubernetes example (#4948, @jianyuan)

Small bug fixes and doc updates (#5119, #5107, #5105, #5103, #5085, #5088, #5051, #5081, #5039, #5073, #5072, #5066, #5064, #5063, #5060, #4718, #5053, #5052, #5041, #5043, #5047, #5036, #5037, #5029, #5031, #5032, #5030, #5007, #5019, #5014, #5008, #4998, #4985, #4984, #4970, #4966, #4980, #4967, #4978, #4979, #4968, #4976, #4975, #4934, #4956, #4938, #4950, #4946, #4939, #4913, #4940, #4935, @harupy; #5095, #5070, #5002, #4958, #4945, @BenWilson2; #5099, @chaosddp; #5005, @you-n-g; #5042, #4952, @shrinath-suresh; #4962, #4995, @WeichenXu123; #5010, @lichenran1234; #5000, @wentinghu; #5111, @alexott; #5102, #5024, #5011, #4959, @dbczumar; #5075, #5044, #5026, #4997, #4964, #4989, @liangz1; #4999, @stevenchen-db)

## 1.21.0 (2021-10-23)

MLflow 1.21.0 includes several major features and improvements:

Features:

- [UI] Add a diff-only toggle to the runs table for filtering out columns with constant values (#4862, @marijncv)
- [UI] Add a duration column to the runs table (#4840, @marijncv)
- [UI] Display the default column sorting order in the runs table (#4847, @marijncv)
- [UI] Add `start_time` and `duration` information to exported runs CSV (#4851, @marijncv)
- [UI] Add lifecycle stage information to the run page (#4848, @marijncv)
- [UI] Collapse run page sections by default for space efficiency, limit artifact previews to 50MB (#4917, @dbczumar)
- [Tracking] Introduce autologging capabilities for PaddlePaddle model training (#4751, @jinminhao)
- [Tracking] Add an optional tags field to the CreateExperiment API (#4788, @dbczumar; #4795, @apurva-koti)
- [Tracking] Add support for deleting artifacts from SFTP stores via the `mlflow gc` CLI (#4670, @afaul)
- [Tracking] Support AzureDefaultCredential for authenticating with Azure artifact storage backends (#4002, @marijncv)
- [Models] Upgrade the fastai model flavor to support fastai V2 (`>=2.4.1`) (#4715, @jinzhang21)
- [Models] Introduce an `mlflow.prophet` model flavor for Prophet time series models (#4773, @BenWilson2)
- [Models] Introduce a CLI for publishing MLflow Models to the SageMaker Model Registry (#4669, @jinnig)
- [Models] Print a warning when inferred model dependencies are not available on PyPI (#4891, @dbczumar)
- [Models, Projects] Add `MLFLOW_CONDA_CREATE_ENV_CMD` for customizing Conda environment creation (#4746, @giacomov)

Bug fixes and documentation updates:

- [UI] Fix an issue where column selections made in the runs table were persisted across experiments (#4926, @sunishsheth2009)
- [UI] Fix an issue where the text `null` was displayed in the runs table column ordering dropdown (#4924, @harupy)
- [UI] Fix a bug causing the metric plot view to display NaN values upon click (#4858, @arpitjasa-db)
- [Tracking] Fix a model load failure for paths containing spaces or special characters on UNIX systems (#4890, @BenWilson2)
- [Tracking] Correct a migration issue that impacted usage of MLflow Tracking with SQL Server (#4880, @marijncv)
- [Tracking] Spark datasource autologging tags now respect the maximum allowable size for MLflow Tracking (#4809, @dbczumar)
- [Model Registry] Add previously-missing certificate sources for Model Registry REST API requests (#4731, @ericgosno91)
- [Model Registry] Throw an exception when users supply invalid Model Registry URIs for Databricks (#4877, @yunpark93)
- [Scoring] Fix a schema enforcement error that incorrectly cast date-like strings to datetime objects (#4902, @wentinghu)
- [Docs] Expand the documentation for the MLflow Skinny Client (#4113, @eedeleon)

Small bug fixes and doc updates (#4928, #4919, #4927, #4922, #4914, #4899, #4893, #4894, #4884, #4864, #4823, #4841, #4817, #4796, #4797, #4767, #4768, #4757, @harupy; #4863, #4838, @marijncv; #4834, @ksaur; #4772, @louisguitton; #4801, @twsl; #4929, #4887, #4856, #4843, #4789, #4780, @WeichenXu123; #4769, @Ark-kun; #4898, #4756, @apurva-koti; #4784, @lakshikaparihar; #4855, @ianshan0915; #4790, @eedeleon; #4931, #4857, #4846, 4777, #4748, @dbczumar)

## 1.20.2 (2021-09-03)

MLflow 1.20.2 is a patch release containing the following features and bug fixes:

Features:

- Enabled auto dependency inference in spark flavor in autologging (#4759, @harupy)

Bug fixes and documentation updates:

- Increased MLflow client HTTP request timeout from 10s to 120s (#4764, @jinzhang21)
- Fixed autologging compatibility bugs with TensorFlow and Keras version `2.6.0` (#4766, @dbczumar)

Small bug fixes and doc updates (#4770, @WeichenXu123)

## 1.20.1 (2021-08-26)

MLflow 1.20.1 is a patch release containing the following bug fixes:

- Avoid calling `importlib_metadata.packages_distributions` upon `mlflow.utils.requirements_utils` import (#4741, @dbczumar)
- Avoid depending on `importlib_metadata==4.7.0` (#4740, @dbczumar)

## 1.20.0 (2021-08-25)

MLflow 1.20.0 includes several major features and improvements:

Features:

- Autologging for scikit-learn now records post training metrics when scikit-learn evaluation APIs, such as `sklearn.metrics.mean_squared_error`, are called (#4491, #4628 #4638, @WeichenXu123)
- Autologging for PySpark ML now records post training metrics when model evaluation APIs, such as `Evaluator.evaluate()`, are called (#4686, @WeichenXu123)
- Add `pip_requirements` and `extra_pip_requirements` to `mlflow.*.log_model` and `mlflow.*.save_model` for directly specifying the pip requirements of the model to log / save (#4519, #4577, #4602, @harupy)
- Added `stdMetrics` entries to the training metrics recorded during PySpark CrossValidator autologging (#4672, @WeichenXu123)
- MLflow UI updates:
  1. Improved scalability of the parallel coordinates plot for run performance comparison,
  2. Added support for filtering runs based on their start time on the experiment page,
  3. Added a dropdown for runs table column sorting on the experiment page,
  4. Upgraded the AG Grid plugin, which is used for runs table loading on the experiment page, to version 25.0.0,
  5. Fixed a bug on the experiment page that caused the metrics section of the runs table to collapse when selecting columns from other table sections (#4712, @dbczumar)
- Added support for distributed execution to autologging for PyTorch Lightning (#4717, @dbczumar)
- Expanded R support for Model Registry functionality (#4527, @bramrodenburg)
- Added model scoring server support for defining custom prediction response wrappers (#4611, @Ark-kun)
- `mlflow.*.log_model` and `mlflow.*.save_model` now automatically infer the pip requirements of the model to log / save based on the current software environment (#4518, @harupy)
- Introduced support for running Sagemaker Batch Transform jobs with MLflow Models (#4410, #4589, @YQ-Wang)

Bug fixes and documentation updates:

- Deprecate `requirements_file` argument for `mlflow.*.save_model` and `mlflow.*.log_model` (#4620, @harupy)
- set nextPageToken to null (#4729, @harupy)
- Fix a bug in MLflow UI where the pagination token for run search is not refreshed when switching experiments (#4709, @harupy)
- Fix a bug in the model scoring server that rejected requests specifying a valid `Content-Type` header with the charset parameter (#4609, @Ark-kun)
- Fixed a bug that caused SQLAlchemy backends to exhaust DB connections. (#4663, @arpitjasa-db)
- Improve docker build procedures to raise exceptions if docker builds fail (#4610, @Ark-kun)
- Disable autologging for scikit-learn `cross_val_*` APIs, which are incompatible with autologging (#4590, @WeichenXu123)
- Deprecate MLflow Models support for fast.ai V1 (#4728, @dbczumar)
- Deprecate the old Azure ML deployment APIs `mlflow.azureml.cli.build_image` and `mlflow.azureml.build_image` (#4646, @trangevi)
- Deprecate MLflow Models support for TensorFlow < 2.0 and Keras < 2.3 (#4716, @harupy)

Small bug fixes and doc updates (#4730, #4722, #4725, #4723, #4703, #4710, #4679, #4694, #4707, #4708, #4706, #4705, #4625, #4701, #4700, #4662, #4699, #4682, #4691, #4684, #4683, #4675, #4666, #4648, #4653, #4651, #4641, #4649, #4627, #4637, #4632, #4634, #4621, #4619, #4622, #4460, #4608, #4605, #4599, #4600, #4581, #4583, #4565, #4575, #4564, #4580, #4572, #4570, #4574, #4576, #4568, #4559, #4537, #4542, @harupy; #4698, #4573, @Ark-kun; #4674, @kvmakes; #4555, @vagoston; #4644, @zhengjxu; #4690, #4588, @apurva-koti; #4545, #4631, #4734, @WeichenXu123; #4633, #4292, @shrinath-suresh; #4711, @jinzhang21; #4688, @murilommen; #4635, @ryan-duve; #4724, #4719, #4640, #4639, #4629, #4612, #4613, #4586, @dbczumar)

## 1.19.0 (2021-07-14)

MLflow 1.19.0 includes several major features and improvements:

Features:

- Add support for plotting per-class feature importance computed on linear boosters in XGBoost autologging (#4523, @dbczumar)
- Add `mlflow_create_registered_model` and `mlflow_delete_registered_model` for R to create/delete registered models.
- Add support for setting tags while resuming a run (#4497, @dbczumar)
- MLflow UI updates (#4490, @sunishsheth2009)

  - Add framework for internationalization support.
  - Move metric columns before parameter and tag columns in the runs table.
  - Change the display format of run start time to elapsed time (e.g. 3 minutes ago) from timestamp (e.g. 2021-07-14 14:02:10) in the runs table.

Bug fixes and documentation updates:

- Fix a bug causing MLflow UI to crash when sorting a column containing both `NaN` and empty values (#3409, @harupy)

Small bug fixes and doc updates (#4541, #4534, #4533, #4517, #4508, #4513, #4512, #4509, #4503, #4486, #4493, #4469, @harupy; #4458, @KasirajanA; #4501, @jimmyxu-db; #4521, #4515, @jerrylian-db; #4359, @shrinath-suresh; #4544, @WeichenXu123; #4549, @smurching; #4554, @derkomai; #4506, @tomasatdatabricks; #4551, #4516, #4494, @dbczumar; #4511, @keypointt)

## 1.18.0 (2021-06-18)

MLflow 1.18.0 includes several major features and improvements:

Features:

- Autologging performance improvements for XGBoost, LightGBM, and scikit-learn (#4416, #4473, @dbczumar)
- Add new PaddlePaddle flavor to MLflow Models (#4406, #4439, @jinminhao)
- Introduce paginated ListExperiments API (#3881, @wamartin-aml)
- Include Runtime version for MLflow Models logged on Databricks (#4421, @stevenchen-db)
- MLflow Models now log dependencies in pip requirements.txt format, in addition to existing conda format (#4409, #4422, @stevenchen-db)
- Add support for limiting the number child runs created by autologging for scikit-learn hyperparameter search models (#4382, @mohamad-arabi)
- Improve artifact upload / download performance on Databricks (#4260, @dbczumar)
- Migrate all model dependencies from conda to "pip" section (#4393, @WeichenXu123)

Bug fixes and documentation updates:

- Fix an MLflow UI bug that caused git source URIs to be rendered improperly (#4403, @takabayashi)
- Fix a bug that prevented reloading of MLflow Models based on the TensorFlow SavedModel format (#4223) (#4319, @saschaschramm)
- Fix a bug in the behavior of `KubernetesSubmittedRun.get_status()` for Kubernetes MLflow Project runs (#3962) (#4159, @jcasse)
- Fix a bug in TLS verification for MLflow artifact operations on S3 (#4047, @PeterSulcs)
- Fix a bug causing the MLflow server to crash after deletion of the default experiment (#4352, @asaf400)
- Fix a bug causing `mlflow models serve` to crash on Windows 10 (#4377, @simonvanbernem)
- Fix a crash in runs search when ordering by metric values against the MSSQL backend store (#2551) (#4238, @naor2013)
- Fix an autologging incompatibility issue with TensorFlow 2.5 (#4371, @dbczumar)
- Fix a bug in the `disable_for_unsupported_versions` autologging argument that caused library versions to be incorrectly compared (#4303, @WeichenXu123)

Small bug fixes and doc updates (#4405, @mohamad-arabi; #4455, #4461, #4459, #4464, #4453, #4444, #4449, #4301, #4424, #4418, #4417, #3759, #4398, #4389, #4386, #4385, #4384, #4380, #4373, #4378, #4372, #4369, #4348, #4364, #4363, #4349, #4350, #4174, #4285, #4341, @harupy; #4446, @kHarshit; #4471, @AveshCSingh; #4435, #4440, #4368, #4360, @WeichenXu123; #4431, @apurva-koti; #4428, @stevenchen-db; #4467, #4402, #4261, @dbczumar)

## 1.17.0 (2021-05-07)

MLflow 1.17.0 includes several major features and improvements:

Features:

- Add support for hyperparameter-tuning models to `mlflow.pyspark.ml.autolog()` (#4270, @WeichenXu123)

Bug fixes and documentation updates:

- Fix PyTorch Lightning callback definition for compatibility with PyTorch Lightning 1.3.0 (#4333, @dbczumar)
- Fix a bug in scikit-learn autologging that omitted artifacts for unsupervised models (#4325, @dbczumar)
- Support logging `datetime.date` objects as part of model input examples (#4313, @vperiyasamy)
- Implement HTTP request retries in the MLflow Java client for 500-level responses (#4311, @dbczumar)
- Include a community code of conduct (#4310, @dennyglee)

Small bug fixes and doc updates (#4276, #4263, @WeichenXu123; #4289, #4302, #3599, #4287, #4284, #4265, #4266, #4275, #4268, @harupy; #4335, #4297, @dbczumar; #4324, #4320, @tleyden)

## 1.16.0 (2021-04-22)

MLflow 1.16.0 includes several major features and improvements:

Features:

- Add `mlflow.pyspark.ml.autolog()` API for autologging of `pyspark.ml` estimators (#4228, @WeichenXu123)
- Add `mlflow.catboost.log_model`, `mlflow.catboost.save_model`, `mlflow.catboost.load_model` APIs for CatBoost model persistence (#2417, @harupy)
- Enable `mlflow.pyfunc.spark_udf` to use column names from model signature by default (#4236, @Loquats)
- Add `datetime` data type for model signatures (#4241, @vperiyasamy)
- Add `mlflow.sklearn.eval_and_log_metrics` API that computes and logs metrics for the given scikit-learn model and labeled dataset. (#4218, @alkispoly-db)

Bug fixes and documentation updates:

- Fix a database migration error for PostgreSQL (#4211, @dolfinus)
- Fix autologging silent mode bugs (#4231, @dbczumar)

Small bug fixes and doc updates (#4255, #4252, #4254, #4253, #4242, #4247, #4243, #4237, #4233, @harupy; #4225, @dmatrix; #4207, @shrinath-suresh; #4264, @WeichenXu123; #3884, #3866, #3885, @ankan94; #4274, #4216, @dbczumar)

## 1.15.0 (2021-03-26)

MLflow 1.15.0 includes several features, bug fixes and improvements. Notably, it includes a number of improvements to MLflow autologging:

Features:

- Add `silent=False` option to all autologging APIs, to allow suppressing MLflow warnings and logging statements during autologging setup and training (#4173, @dbczumar)
- Add `disable_for_unsupported_versions=False` option to all autologging APIs, to disable autologging for versions of ML frameworks that have not been explicitly tested against the current version of the MLflow client (#4119, @WeichenXu123)

Bug fixes:

- Autologged runs are now terminated when execution is interrupted via SIGINT (#4200, @dbczumar)
- The R `mlflow_get_experiment` API now returns the same tag structure as `mlflow_list_experiments` and `mlflow_get_run` (#4017, @lorenzwalthert)
- Fix bug where `mlflow.tensorflow.autolog` would previously mutate the user-specified callbacks list when fitting `tf.keras` models (#4195, @dbczumar)
- Fix bug where SQL-backed MLflow tracking server initialization failed when using the MLflow skinny client (#4161, @eedeleon)
- Model version creation (e.g. via `mlflow.register_model`) now fails if the model version status is not READY (#4114, @ankit-db)

Small bug fixes and doc updates (#4191, #4149, #4162, #4157, #4155, #4144, #4141, #4138, #4136, #4133, #3964, #4130, #4118, @harupy; #4139, @WeichenXu123; #4193, @smurching; #4029, @architkulkarni; #4134, @xhochy; #4116, @wenleix; #4160, @wentinghu; #4203, #4184, #4167, @dbczumar)

## 1.14.1 (2021-03-01)

MLflow 1.14.1 is a patch release containing the following bug fix:

- Fix issues in handling flexible numpy datatypes in TensorSpec (#4147, @arjundc-db)

## 1.14.0 (2021-02-18)

MLflow 1.14.0 includes several major features and improvements:

- MLflow's model inference APIs (`mlflow.pyfunc.predict`), built-in model serving tools (`mlflow models serve`), and model signatures now support tensor inputs. In particular, MLflow now provides built-in support for scoring PyTorch, TensorFlow, Keras, ONNX, and Gluon models with tensor inputs. For more information, see https://mlflow.org/docs/latest/models.html#deploy-mlflow-models (#3808, #3894, #4084, #4068 @wentinghu; #4041 @tomasatdatabricks, #4099, @arjundc-db)
- Add new `mlflow.shap.log_explainer`, `mlflow.shap.load_explainer` APIs for logging and loading `shap.Explainer` instances (#3989, @vivekchettiar)
- The MLflow Python client is now available with a reduced dependency set via the `mlflow-skinny` PyPI package (#4049, @eedeleon)
- Add new `RequestHeaderProvider` plugin interface for passing custom request headers with REST API requests made by the MLflow Python client (#4042, @jimmyxu-db)
- `mlflow.keras.log_model` now saves models in the TensorFlow SavedModel format by default instead of the older Keras H5 format (#4043, @harupy)
- `mlflow_log_model` now supports logging MLeap models in R (#3819, @yitao-li)
- Add `mlflow.pytorch.log_state_dict`, `mlflow.pytorch.load_state_dict` for logging and loading PyTorch state dicts (#3705, @shrinath-suresh)
- `mlflow gc` can now garbage-collect artifacts stored in S3 (#3958, @sklingel)

Bug fixes and documentation updates:

- Enable autologging for TensorFlow estimators that extend `tensorflow.compat.v1.estimator.Estimator` (#4097, @mohamad-arabi)
- Fix for universal autolog configs overriding integration-specific configs (#4093, @dbczumar)
- Allow `mlflow.models.infer_signature` to handle dataframes containing `pandas.api.extensions.ExtensionDtype` (#4069, @caleboverman)
- Fix bug where `mlflow_restore_run` doesn't propagate the `client` parameter to `mlflow_get_run` (#4003, @yitao-li)
- Fix bug where scoring on served model fails when request data contains a string that looks like URL and pandas version is later than 1.1.0 (#3921, @Secbone)
- Fix bug causing `mlflow_list_experiments` to fail listing experiments with tags (#3942, @lorenzwalthert)
- Fix bug where metrics plots are computed from incorrect target values in scikit-learn autologging (#3993, @mtrencseni)
- Remove redundant / verbose Python event logging message in autologging (#3978, @dbczumar)
- Fix bug where `mlflow_load_model` doesn't load metadata associated to MLflow model flavor in R (#3872, @yitao-li)
- Fix `mlflow.spark.log_model`, `mlflow.spark.load_model` APIs on passthrough-enabled environments against ACL'd artifact locations (#3443, @smurching)

Small bug fixes and doc updates (#4102, #4101, #4096, #4091, #4067, #4059, #4016, #4054, #4052, #4051, #4038, #3992, #3990, #3981, #3949, #3948, #3937, #3834, #3906, #3774, #3916, #3907, #3938, #3929, #3900, #3902, #3899, #3901, #3891, #3889, @harupy; #4014, #4001, @dmatrix; #4028, #3957, @dbczumar; #3816, @lorenzwalthert; #3939, @pauldj54; #3740, @jkthompson; #4070, #3946, @jimmyxu-db; #3836, @t-henri; #3982, @neo-anderson; #3972, #3687, #3922, @eedeleon; #4044, @WeichenXu123; #4063, @yitao-li; #3976, @whiteh; #4110, @tomasatdatabricks; #4050, @apurva-koti; #4100, #4084, @wentinghu; #3947, @vperiyasamy; #4021, @trangevi; #3773, @ankan94; #4090, @jinzhang21; #3918, @danielfrg)

## 1.13.1 (2020-12-30)

MLflow 1.13.1 is a patch release containing bug fixes and small changes:

- Fix bug causing Spark autologging to ignore configuration options specified by `mlflow.autolog()` (#3917, @dbczumar)
- Fix bugs causing metrics to be dropped during TensorFlow autologging (#3913, #3914, @dbczumar)
- Fix incorrect value of optimizer name parameter in autologging PyTorch Lightning (#3901, @harupy)
- Fix model registry database `allow_null_for_run_id` migration failure affecting MySQL databases (#3836, @t-henri)
- Fix failure in `transition_model_version_stage` when uncanonical stage name is passed (#3929, @harupy)
- Fix an undefined variable error causing AzureML model deployment to fail (#3922, @eedeleon)
- Reclassify scikit-learn as a pip dependency in MLflow Model conda environments (#3896, @harupy)
- Fix experiment view crash and artifact view inconsistency caused by artifact URIs with redundant slashes (#3928, @dbczumar)

## 1.13 (2020-12-22)

MLflow 1.13 includes several major features and improvements:

Features:

New fluent APIs for logging in-memory objects as artifacts:

- Add `mlflow.log_text` which logs text as an artifact (#3678, @harupy)
- Add `mlflow.log_dict` which logs a dictionary as an artifact (#3685, @harupy)
- Add `mlflow.log_figure` which logs a figure object as an artifact (#3707, @harupy)
- Add `mlflow.log_image` which logs an image object as an artifact (#3728, @harupy)

UI updates / fixes (#3867, @smurching):

- Add model version link in compact experiment table view
- Add logged/registered model links in experiment runs page view
- Enhance artifact viewer for MLflow models
- Model registry UI settings are now persisted across browser sessions
- Add model version `description` field to model version table

Autologging enhancements:

- Improve robustness of autologging integrations to exceptions (#3682, #3815, dbczumar; #3860, @mohamad-arabi; #3854, #3855, #3861, @harupy)
- Add `disable` configuration option for autologging (#3682, #3815, dbczumar; #3838, @mohamad-arabi; #3854, #3855, #3861, @harupy)
- Add `exclusive` configuration option for autologging (#3851, @apurva-koti; #3869, @dbczumar)
- Add `log_models` configuration option for autologging (#3663, @mohamad-arabi)
- Set tags on autologged runs for easy identification (and add tags to start_run) (#3847, @dbczumar)

More features and improvements:

- Allow Keras models to be saved with `SavedModel` format (#3552, @skylarbpayne)
- Add support for `statsmodels` flavor (#3304, @olbapjose)
- Add support for nested-run in mlflow R client (#3765, @yitao-li)
- Deploying a model using `mlflow.azureml.deploy` now integrates better with the AzureML tracking/registry. (#3419, @trangevi)
- Update schema enforcement to handle integers with missing values (#3798, @tomasatdatabricks)

Bug fixes and documentation updates:

- When running an MLflow Project on Databricks, the version of MLflow installed on the Databricks cluster will now match the version used to run the Project (#3880, @FlorisHoogenboom)
- Fix bug where metrics are not logged for single-epoch `tf.keras` training sessions (#3853, @dbczumar)
- Reject boolean types when logging MLflow metrics (#3822, @HCoban)
- Fix alignment of Keras / `tf.Keras` metric history entries when `initial_epoch` is different from zero. (#3575, @garciparedes)
- Fix bugs in autologging integrations for newer versions of TensorFlow and Keras (#3735, @dbczumar)
- Drop global `filterwwarnings` module at import time (#3621, @jogo)
- Fix bug that caused preexisting Python loggers to be disabled when using MLflow with the SQLAlchemyStore (#3653, @arthury1n)
- Fix `h5py` library incompatibility for exported Keras models (#3667, @tomasatdatabricks)

Small changes, bug fixes and doc updates (#3887, #3882, #3845, #3833, #3830, #3828, #3826, #3825, #3800, #3809, #3807, #3786, #3794, #3731, #3776, #3760, #3771, #3754, #3750, #3749, #3747, #3736, #3701, #3699, #3698, #3658, #3675, @harupy; #3723, @mohamad-arabi; #3650, #3655, @shrinath-suresh; #3850, #3753, #3725, @dmatrix; ##3867, #3670, #3664, @smurching; #3681, @sueann; #3619, @andrewnitu; #3837, @javierluraschi; #3721, @szczeles; #3653, @arthury1n; #3883, #3874, #3870, #3877, #3878, #3815, #3859, #3844, #3703, @dbczumar; #3768, @wentinghu; #3784, @HCoban; #3643, #3649, @arjundc-db; #3864, @AveshCSingh, #3756, @yitao-li)

## 1.12.1 (2020-11-19)

MLflow 1.12.1 is a patch release containing bug fixes and small changes:

- Fix `run_link` for cross-workspace model versions (#3681, @sueann)
- Remove hard dependency on matplotlib for sklearn autologging (#3703, @dbczumar)
- Do not disable existing loggers when initializing alembic (#3653, @arthury1n)

## 1.12.0 (2020-11-10)

MLflow 1.12.0 includes several major features and improvements, in particular a number of improvements to autologging and MLflow's Pytorch integrations:

Features:

Autologging:

- Add universal `mlflow.autolog` which enables autologging for all supported integrations (#3561, #3590, @andrewnitu)
- Add `mlflow.pytorch.autolog` API for automatic logging of metrics, params, and models from Pytorch Lightning training (#3601, @shrinath-suresh, #3636, @karthik-77). This API is also enabled by `mlflow.autolog`.
- Scikit-learn, XGBoost, and LightGBM autologging now support logging model signatures and input examples (#3386, #3403, #3449, @andrewnitu)
- `mlflow.sklearn.autolog` now supports logging metrics (e.g. accuracy) and plots (e.g. confusion matrix heat map) (#3423, #3327, @willzhan-db, @harupy)

PyTorch:

- `mlflow.pytorch.log_model`, `mlflow.pytorch.load_model` now support logging/loading TorchScript models (#3557, @shrinath-suresh)
- `mlflow.pytorch.log_model` supports passing `requirements_file` & `extra_files` arguments to log additional artifacts along with a model (#3436, @shrinath-suresh)

More features and improvements:

- Add `mlflow.shap.log_explanation` for logging model explanations generated by SHAP (#3513, @harupy)
- `log_model` and `create_model_version` now supports an `await_creation_for` argument (#3376, @andychow-db)
- Put preview paths before non-preview paths for backwards compatibility (#3648, @sueann)
- Clean up model registry endpoint and client method definitions (#3610, @sueann)
- MLflow deployments plugin now supports 'predict' CLI command (#3597, @shrinath-suresh)
- Support H2O for R (#3416, @yitao-li)
- Add `MLFLOW_S3_IGNORE_TLS` environment variable to enable skipping TLS verification of S3 endpoint (#3345, @dolfinus)

Bug fixes and documentation updates:

- Ensure that results are synced across distributed processes if ddp enabled (no-op else) (#3651, @SeanNaren)
- Remove optimizer step override to ensure that all accelerator cases are covered by base module (#3635, @SeanNaren)
- Fix `AttributeError` in keras autologgging (#3611, @sephib)
- Scikit-learn autologging: Exclude feature extraction / selection estimator (#3600, @dbczumar)
- Scikit-learn autologging: Fix behavior when a child and its parent are both patched (#3582, @dbczumar)
- Fix a bug where `lightgbm.Dataset(None)` fails after running `mlflow.lightgbm.autolog` (#3594, @harupy)
- Fix a bug where `xgboost.DMatrix(None)` fails after running `mlflow.xgboost.autolog` (#3584, @harupy)
- Pass `docker_args` in non-synchronous mlflow project runs (#3563, @alfozan)
- Fix a bug of `FTPArtifactRepository.log_artifacts` with `artifact_path` keyword argument (issue #3388) (#3391, @kzm4269)
- Exclude preprocessing & imputation steps from scikit-learn autologging (#3491, @dbczumar)
- Fix duplicate stderr logging during artifact logging and project execution in the R client (#3145, @yitao-li)
- Don't call `atexit.register(_flush_queue)` in `__main__` scope of `mlflow/tensorflow.py` (#3410, @harupy)
- Fix for restarting terminated run not setting status correctly (#3329, @apurva-koti)
- Fix model version run_link URL for some Databricks regions (#3417, @sueann)
- Skip JSON validation when endpoint is not MLflow REST API (#3405, @harupy)
- Document `mlflow-torchserve` plugin (#3634, @karthik-77)
- Add `mlflow-elasticsearchstore` to the doc (#3462, @AxelVivien25)
- Add code snippets for fluent and MlflowClient APIs (#3385, #3437, #3489 #3573, @dmatrix)
- Document `mlflow-yarn` backend (#3373, @fhoering)
- Fix a breakage in loading Tensorflow and Keras models (#3667, @tomasatdatabricks)

Small bug fixes and doc updates (#3607, #3616, #3534, #3598, #3542, #3568, #3349, #3554, #3544, #3541, #3533, #3535, #3516, #3512, #3497, #3522, #3521, #3492, #3502, #3434, #3422, #3394, #3387, #3294, #3324, #3654, @harupy; #3451, @jgc128; #3638, #3632, #3608, #3452, #3399, @shrinath-suresh; #3495, #3459, #3662, #3668, #3670 @smurching; #3488, @edgan8; #3639, @karthik-77; #3589, #3444, #3276, @lorenzwalthert; #3538, #3506, #3509, #3507, #3510, #3508, @rahulporuri; #3504, @sbrugman; #3486, #3466, @apurva-koti; #3477, @juntai-zheng; #3617, #3609, #3605, #3603, #3560, @dbczumar; #3411, @danielvdende; #3377, @willzhan-db; #3420, #3404, @andrewnitu; #3591, @mateiz; #3465, @abawchen; #3543, @emptalk; #3302, @bramrodenburg; #3468, @ghisvail; #3496, @extrospective; #3549, #3501, #3435, @yitao-li; #3243, @OlivierBondu; #3439, @andrewnitu; #3651, #3635 @SeanNaren, #3470, @ankit-db)

## 1.11.0 (2020-08-31)

MLflow 1.11.0 includes several major features and improvements:

Features:

- New `mlflow.sklearn.autolog()` API for automatic logging of metrics, params, and models from scikit-learn model training (#3287, @harupy; #3323, #3358 @dbczumar)
- Registered model & model version creation APIs now support specifying an initial `description` (#3271, @sueann)
- The R `mlflow_log_model` and `mlflow_load_model` APIs now support XGBoost models (#3085, @lorenzwalthert)
- New `mlflow.list_run_infos` fluent API for listing run metadata (#3183, @trangevi)
- Added section for visualizing and comparing model schemas to model version and model-version-comparison UIs (#3209, @zhidongqu-db)
- Enhanced support for using the model registry across Databricks workspaces: support for registering models to a Databricks workspace from outside the workspace (#3119, @sueann), tracking run-lineage of these models (#3128, #3164, @ankitmathur-db; #3187, @harupy), and calling `mlflow.<flavor>.load_model` against remote Databricks model registries (#3330, @sueann)
- UI support for setting/deleting registered model and model version tags (#3187, @harupy)
- UI support for archiving existing staging/production versions of a model when transitioning a new model version to staging/production (#3134, @harupy)

Bug fixes and documentation updates:

- Fixed parsing of MLflow project parameter values containing'=' (#3347, @dbczumar)
- Fixed a bug preventing listing of WASBS artifacts on the latest version of Azure Blob Storage (12.4.0) (#3348, @dbczumar)
- Fixed a bug where artifact locations become malformed when using an SFTP file store in Windows (#3168, @harupy)
- Fixed bug where `list_artifacts` returned incorrect results on GCS, preventing e.g. loading SparkML models from GCS (#3242, @santosh1994)
- Writing and reading artifacts via `MlflowClient` to a DBFS location in a Databricks tracking server specified through the `tracking_uri` parameter during the initialization of `MlflowClient` now works properly (#3220, @sueann)
- Fixed bug where `FTPArtifactRepository` returned artifact locations as absolute paths, rather than paths relative to the artifact repository root (#3210, @shaneing), and bug where calling `log_artifacts` against an FTP artifact location copied the logged directory itself into the FTP location, rather than the contents of the directory.
- Fixed bug where Databricks project execution failed due to passing of GET request params as part of the request body rather than as query parameters (#2947, @cdemonchy-pro)
- Fix bug where artifact viewer did not correctly render PDFs in MLflow 1.10 (#3172, @ankitmathur-db)
- Fixed parsing of `order_by` arguments to MLflow search APIs when ordering by fields whose names contain spaces (#3118, @jdlesage)
- Fixed bug where MLflow model schema enforcement raised exceptions when validating string columns using pandas >= 1.0 (#3130, @harupy)
- Fixed bug where `mlflow.spark.log_model` did not save model signature and input examples (#3151, @harupy)
- Fixed bug in runs UI where tags table did not reflect deletion of tags. (#3135, @ParseDark)
- Added example illustrating the use of RAPIDS with MLflow (#3028, @drobison00)

Small bug fixes and doc updates (#3326, #3344, #3314, #3289, #3225, #3288, #3279, #3265, #3263, #3260, #3255, #3267, #3266, #3264, #3256, #3253, #3231, #3245, #3191, #3238, #3192, #3188, #3189, #3180, #3178, #3166, #3181, #3142, #3165, #2960, #3129, #3244, #3359 @harupy; #3236, #3141, @AveshCSingh; #3295, #3163, @arjundc-db; #3241, #3200, @zhidongqu-db; #3338, #3275, @sueann; #3020, @magnus-m; #3322, #3219, @dmatrix; #3341, #3179, #3355, #3360, #3363 @smurching; #3124, @jdlesage; #3232, #3146, @ankitmathur-db; #3140, @andreakress; #3062, @cafeal; #3193, @tomasatdatabricks; 3115, @fhoering; #3328, @apurva-koti; #3046, @OlivierBondu; #3194, #3158, @dmatrix; #3250, @shivp950; #3259, @simonhessner; #3357 @dbczumar)

## 1.10.0 (2020-07-20)

MLflow 1.10.0 includes several major features and improvements, in particular the release of several new model registry Python client APIs.

Features:

- `MlflowClient.transition_model_version_stage` now supports an
  `archive_existing_versions` argument for archiving existing staging or production model
  versions when transitioning a new model version to staging or production (#3095, @harupy)
- Added `set_registry_uri`, `get_registry_uri` APIs. Setting the model registry URI causes
  fluent APIs like `mlflow.register_model` to communicate with the model registry at the specified
  URI (#3072, @sueann)
- Added paginated `MlflowClient.search_registered_models` API (#2939, #3023, #3027 @ankitmathur-db; #2966, @mparkhe)
- Added syntax highlighting when viewing text files (YAML etc) in the MLflow runs UI (#3041, @harupy)
- Added REST API and Python client support for setting and deleting tags on model versions and registered models,
  via the `MlflowClient.create_registered_model`, `MlflowClient.create_model_version`,
  `MlflowClient.set_registered_model_tag`, `MlflowClient.set_model_version_tag`,
  `MlflowClient.delete_registered_model_tag`, and `MlflowClient.delete_model_version_tag` APIs (#3094, @zhidongqu-db)

Bug fixes and documentation updates:

- Removed usage of deprecated `aws ecr get-login` command in `mlflow.sagemaker` (#3036, @mrugeles)
- Fixed bug where artifacts could not be viewed and downloaded from the artifact UI when using
  Azure Blob Storage (#3014, @Trollgeir)
- Databricks credentials are now propagated to the project subprocess when running MLflow projects
  within a notebook (#3035, @smurching)
- Added docs explaining how to fetching an MLflow model from the model registry (#3000, @andychow-db)

Small bug fixes and doc updates (#3112, #3102, #3089, #3103, #3096, #3090, #3049, #3080, #3070, #3078, #3083, #3051, #3050, #2875, #2982, #2949, #3121 @harupy; #3082, @ankitmathur-db; #3084, #3019, @smurching)

## 1.9.1 (2020-06-25)

MLflow 1.9.1 is a patch release containing a number of bug-fixes and improvements:

Bug fixes and improvements:

- Fixes `AttributeError` when pickling an instance of the Python `MlflowClient` class (#2955, @Polyphenolx)
- Fixes bug that prevented updating model-version descriptions in the model registry UI (#2969, @AnastasiaKol)
- Fixes bug where credentials were not properly propagated to artifact CLI commands when logging artifacts from Java to the DatabricksArtifactRepository (#3001, @dbczumar)
- Removes use of new Pandas API in new MLflow model-schema functionality, so that it can be used with older Pandas versions (#2988, @aarondav)

Small bug fixes and doc updates (#2998, @dbczumar; #2999, @arjundc-db)

## 1.9.0 (2020-06-19)

MLflow 1.9.0 includes numerous major features and improvements, and a breaking change to
experimental APIs:

Breaking Changes:

- The `new_name` argument to `MlflowClient.update_registered_model`
  has been removed. Call `MlflowClient.rename_registered_model` instead. (#2946, @mparkhe)
- The `stage` argument to `MlflowClient.update_model_version`
  has been removed. Call `MlflowClient.transition_model_version_stage` instead. (#2946, @mparkhe)

Features (MLflow Models and Flavors)

- `log_model` and `save_model` APIs now support saving model signatures (the model's input and output schema)
  and example input along with the model itself (#2698, #2775, @tomasatdatabricks). Model signatures are used
  to reorder and validate input fields when scoring/serving models using the pyfunc flavor, `mlflow models`
  CLI commands, or `mlflow.pyfunc.spark_udf` (#2920, @tomasatdatabricks and @aarondav)
- Introduce fastai model persistence and autologging APIs under `mlflow.fastai` (#2619, #2689 @antoniomdk)
- Add pluggable `mlflow.deployments` API and CLI for deploying models to custom serving tools, e.g. RedisAI
  (#2327, @hhsecond)
- Enables loading and scoring models whose conda environments include dependencies in conda-forge (#2797, @dbczumar)
- Add support for scoring ONNX-persisted models that return Python lists (#2742, @andychow-db)

Features (MLflow Projects)

- Add plugin interface for executing MLflow projects against custom backends (#2566, @jdlesage)
- Add ability to specify additional cluster-wide Python and Java libraries when executing
  MLflow projects remotely on Databricks (#2845, @pogil)
- Allow running MLflow projects against remote artifacts stored in any location with a corresponding
  ArtifactRepository implementation (Azure Blob Storage, GCS, etc) (#2774, @trangevi)
- Allow MLflow projects running on Kubernetes to specify a different tracking server to log to via the
  `KUBE_MLFLOW_TRACKING_URI` for passing a different tracking server to the kubernetes job (#2874, @catapulta)

Features (UI)

- Significant performance and scalability improvements to metric comparison and scatter plots in
  the UI (#2447, @mjlbach)
- The main MLflow experiment list UI now includes a link to the model registry UI (#2805, @zhidongqu-db),
- Enable viewing PDFs logged as artifacts from the runs UI (#2859, @ankmathur96)
- UI accessibility improvements: better color contrast (#2872, @Zangr), add child roles to DOM elements (#2871, @Zangr)

Features (Tracking Client and Server)

- Adds ability to pass client certs as part of REST API requests when using the tracking or model
  registry APIs. (#2843, @PhilipMay)
- New community plugin: support for storing artifacts in Aliyun (Alibaba Cloud) (#2917, @SeaOfOcean)
- Infer and set content type and encoding of objects when logging models and artifacts to S3 (#2881, @hajapy)
- Adds support for logging artifacts to HDFS Federation ViewFs (#2782, @fhoering)
- Add healthcheck endpoint to the MLflow server at `/health` (#2725, @crflynn)
- Improves performance of default file-based tracking storage backend by using LibYAML (if installed)
  to read experiment and run metadata (#2707, @Higgcz)

Bug fixes and documentation updates:

- Several UI fixes: remove margins around icon buttons (#2827, @harupy),
  fix alignment issues in metric view (#2811, @zhidongqu-db), add handling of `NaN`
  values in metrics plot (#2773, @dbczumar), truncate run ID in the run name when
  comparing multiple runs (#2508, @harupy)
- Database engine URLs are no longer logged when running `mlflow db upgrade` (#2849, @hajapy)
- Updates `log_artifact`, `log_model` APIs to consistently use posix paths, rather than OS-dependent
  paths, when computing artifact subpaths. (#2784, @mikeoconnor0308)
- Fix `ValueError` when scoring `tf.keras` 1.X models using `mlflow.pyfunc.predict` (#2762, @juntai-zheng)
- Fixes conda environment activation bug when running MLflow projects on Windows (#2731, @MynherVanKoek)
- `mlflow.end_run` will now clear the active run even if the run cannot be marked as
  terminated (e.g. because it's been deleted), (#2693, @ahmed-shariff)
- Add missing documentation for `mlflow.spacy` APIs (#2771, @harupy)

Small bug fixes and doc updates (#2919, @willzhan-db; #2940, #2942, #2941, #2943, #2927, #2929, #2926, #2914, #2928, #2913, #2852, #2876, #2808, #2810, #2442, #2780, #2758, #2732, #2734, #2431, #2733, #2716, @harupy; #2915, #2897, @jwgwalton; #2856, @jkthompson; #2962, @hhsecond; #2873, #2829, #2582, @dmatrix; #2908, #2865, #2880, #2866, #2833, #2785, #2723, @smurching; #2906, @dependabot[bot]; #2724, @aarondav; #2896, @ezeeetm; #2864, @tallen94; #2726, @crflynn; #2710, #2951 @mparkhe; #2935, #2921, @ankitmathur-db; #2963, #2739, @dbczumar; #2853, @stat4jason; #2709, #2792, @juntai-zheng @juntai-zheng; #2749, @HiromuHota; #2957, #2911, #2718, @arjundc-db; #2885, @willzhan-db; #2803, #2761, @pogil; #2392, @jnmclarty; #2794, @Zethson; #2766, #2916 @shubham769)

## 1.8.0 (2020-04-16)

MLflow 1.8.0 includes several major features and improvements:

Features:

- Added `mlflow.azureml.deploy` API for deploying MLflow models to AzureML (#2375 @csteegz, #2711, @akshaya-a)
- Added support for case-sensitive LIKE and case-insensitive ILIKE queries (e.g. `'params.framework LIKE '%sklearn%'`) with the SearchRuns API & UI when running against a SQLite backend (#2217, @t-henri; #2708, @mparkhe)
- Improved line smoothing in MLflow metrics UI using exponential moving averages (#2620, @Valentyn1997)
- Added `mlflow.spacy` module with support for logging and loading spaCy models (#2242, @arocketman)
- Parameter values that differ across runs are highlighted in run comparison UI (#2565, @gabrielbretschner)
- Added ability to compare source runs associated with model versions from the registered model UI (#2537, @juntai-zheng)
- Added support for alphanumerical experiment IDs in the UI. (#2568, @jonas)
- Added support for passing arguments to `docker run` when running docker-based MLflow projects (#2608, @ksanjeevan)
- Added Windows support for `mlflow sagemaker build-and-push-container` CLI & API (#2500, @AndreyBulezyuk)
- Improved performance of reading experiment data from local filesystem when LibYAML is installed (#2707, @Higgcz)
- Added a healthcheck endpoint to the REST API server at `/health` that always returns a 200 response status code, to be used to verify health of the server (#2725, @crflynn)
- MLflow metrics UI plots now scale to rendering thousands of points using scattergl (#2447, @mjlbach)

Bug fixes:

- Fixed CLI summary message in `mlflow azureml build_image` CLI (#2712, @dbczumar)
- Updated `examples/flower_classifier/score_images_rest.py` with multiple bug fixes (#2647, @tfurmston)
- Fixed pip not found error while packaging models via `mlflow models build-docker` (#2699, @HiromuHota)
- Fixed bug in `mlflow.tensorflow.autolog` causing erroneous deletion of TensorBoard logging directory (#2670, @dbczumar)
- Fixed a bug that truncated the description of the `mlflow gc` subcommand in `mlflow --help` (#2679, @dbczumar)
- Fixed bug where `mlflow models build-docker` was failing due to incorrect Miniconda download URL (#2685, @michaeltinsley)
- Fixed a bug in S3 artifact logging functionality where `MLFLOW_S3_ENDPOINT_URL` was ignored (#2629, @poppash)
- Fixed a bug where Sqlite in-memory was not working as a tracking backend store by modifying DB upgrade logic (#2667, @dbczumar)
- Fixed a bug to allow numerical parameters with values >= 1000 in R `mlflow::mlflow_run()` API (#2665, @lorenzwalthert)
- Fixed a bug where AWS creds was not found in the Windows platform due path differences (#2634, @AndreyBulezyuk)
- Fixed a bug to add pip when necessary in `_mlflow_conda_env` (#2646, @tfurmston)
- Fixed error code to be more meaningful if input to model version is incorrect (#2625, @andychow-db)
- Fixed multiple bugs in model registry (#2638, @aarondav)
- Fixed support for conda env dicts with `mlflow.pyfunc.log_model` (#2618, @dbczumar)
- Fixed a bug where hiding the start time column in the UI would also hide run selection checkboxes (#2559, @harupy)

Documentation updates:

- Added links to source code to mlflow.org (#2627, @harupy)
- Documented fix for pandas-records payload (#2660, @SaiKiranBurle)
- Fixed documentation bug in TensorFlow `load_model` utility (#2666, @pogil)
- Added the missing Model Registry description and link on the first page (#2536, @dmatrix)
- Added documentation for expected datatype for step argument in `log_metric` to match REST API (#2654, @mparkhe)
- Added usage of the model registry to the `log_model` function in `sklearn_elasticnet_wine/train.py` example (#2609, @netanel246)

Small bug fixes and doc updates (#2594, @Trollgeir; #2703,#2709, @juntai-zheng; #2538, #2632, @keigohtr; #2656, #2553, @lorenzwalthert; #2622, @pingsutw; #1391, @sueann; #2613, #2598, #2534, #2723, @smurching; #2652, #2710, @mparkhe; #2706, #2653, #2639, @tomasatdatabricks; #2611, @9dogs; #2700, #2705, @aarondav; #2675, #2540, @mengxr; #2686, @RensDimmendaal; #2694, #2695, #2532, @dbczumar; #2733, #2716, @harupy; #2726, @crflynn; #2582, #2687, @dmatrix)

## 1.7.2 (2020-03-20)

MLflow 1.7.2 is a patch release containing a minor change:

- Pin alembic version to 1.4.1 or below to prevent pep517-related installation errors
  (#2612, @smurching)

## 1.7.1 (2020-03-17)

MLflow 1.7.1 is a patch release containing bug fixes and small changes:

- Remove usage of Nonnull annotations and findbugs dependency in Java package (#2583, @mparkhe)
- Add version upper bound (<=1.3.13) to sqlalchemy dependency in Python package (#2587, @smurching)

Other bugfixes and doc updates (#2595, @mparkhe; #2567, @jdlesage)

## 1.7.0 (2020-03-02)

MLflow 1.7.0 includes several major features and improvements, and some notable breaking changes:

MLflow support for Python 2 is now deprecated and will be dropped in a future release. At that
point, existing Python 2 workflows that use MLflow will continue to work without modification, but
Python 2 users will no longer get access to the latest MLflow features and bugfixes. We recommend
that you upgrade to Python 3 - see https://docs.python.org/3/howto/pyporting.html for a migration
guide.

Breaking changes to Model Registry REST APIs:

Model Registry REST APIs have been updated to be more consistent with the other MLflow APIs. With
this release Model Registry APIs are intended to be stable until the next major version.

- Python and Java client APIs for Model Registry have been updated to use the new REST APIs. When using an MLflow client with a server using updated REST endpoints, you won't need to change any code but will need to upgrade to a new client version. The client APIs contain deprecated arguments, which for this release are backward compatible, but will be dropped in future releases. (#2457, @tomasatdatabricks; #2502, @mparkhe).
- The Model Registry UI has been updated to use the new REST APIs (#2476 @aarondav; #2507, @mparkhe)

Other Features:

- Ability to click through to individual runs from metrics plot (#2295, @harupy)
- Added `mlflow gc` CLI for permanent deletion of runs (#2265, @t-henri)
- Metric plot state is now captured in page URLs for easier link sharing (#2393, #2408, #2498 @smurching; #2459, @harupy)
- Added experiment management to MLflow UI (create/rename/delete experiments) (#2348, @ggliem)
- Ability to search for experiments by name in the UI (#2324, @ggliem)
- MLflow UI page titles now reflect the content displayed on the page (#2420, @AveshCSingh)
- Added a new `LogModel` REST API endpoint for capturing model metadata, and call it from the Python and R clients (#2369, #2430, #2468 @tomasatdatabricks)
- Java Client API to download model artifacts from Model Registry (#2308, @andychow-db)

Bug fixes and documentation updates:

- Updated Model Registry documentation page with code snippets and examples (#2493, @dmatrix; #2517, @harupy)
- Better error message for Model Registry, when using incompatible backend server (#2456, @aarondav)
- matplotlib is no longer required to use XGBoost and LightGBM autologging (#2423, @harupy)
- Fixed bug where matplotlib figures were not closed in XGBoost and LightGBM autologging (#2386, @harupy)
- Fixed parameter reading logic to support param values with newlines in FileStore (#2376, @dbczumar)
- Improve readability of run table column selector nodes (#2388, @dbczumar)
- Validate experiment name supplied to `UpdateExperiment` REST API endpoint (#2357, @ggliem)
- Fixed broken MLflow DB README link in CLI docs (#2377, @dbczumar)
- Change copyright year across docs to 2020 (#2349, @ParseThis)

Small bug fixes and doc updates (#2378, #2449, #2402, #2397, #2391, #2387, #2523, #2527 @harupy; #2314, @juntai-zheng; #2404, @andychow-db; #2343, @pogil; #2366, #2370, #2364, #2356, @AveshCSingh; #2373, #2365, #2363, @smurching; #2358, @jcuquemelle; #2490, @RensDimmendaal; #2506, @dbczumar; #2234 @Zangr; #2359 @lbernickm; #2525, @mparkhe)

## 1.6.0 (2020-01-29)

MLflow 1.6.0 includes several new features, including a better runs table interface, a utility for easier parameter tuning, and automatic logging from XGBoost, LightGBM, and Spark. It also implements a long-awaited fix allowing @ symbols in database URLs. A complete list is below:

Features:

- Adds a new runs table column view based on `ag-grid` which adds functionality for nested runs, serverside sorting, column reordering, highlighting, and more. (#2251, @Zangr)
- Adds contour plot to the run comparison page to better support parameter tuning (#2225, @harupy)
- If you use EarlyStopping with Keras autologging, MLflow now automatically captures the best model trained and the associated metrics (#2301, #2219, @juntai-zheng)
- Adds autologging functionality for LightGBM and XGBoost flavors to log feature importance, metrics per iteration, the trained model, and more. (#2275, #2238, @harupy)
- Adds an experimental mlflow.spark.autolog() API for automatic logging of Spark datasource information to the current active run. (#2220, @smurching)
- Optimizes the file store to load less data from disk for each operation (#2339, @jonas)
- Upgrades from ubuntu:16.04 to ubuntu:18.04 when building a Docker image with `mlflow models build-docker` (#2256, @andychow-db)

Bug fixes and documentation updates:

- Fixes bug when running server against database URLs with @ symbols (#2289, @hershaw)
- Fixes model Docker image build on Windows (#2257, @jahas)
- Documents the SQL Server plugin (#2320, @avflor)
- Adds a help file for the R package (#2259, @lorenzwalthert)
- Adds an example of using the Search API to find the best performing model (#2313, @AveshCSingh)
- Documents how to write and use MLflow plugins (#2270, @smurching)

Small bug fixes and doc updates (#2293, #2328, #2244, @harupy; #2269, #2332, #2306, #2307, #2292, #2267, #2191, #2231, @juntai-zheng; #2325, @shubham769; #2291, @sueann; #2315, #2249, #2288, #2278, #2253, #2181, @smurching; #2342, @tomasatdatabricks; #2245, @dependabot[bot]; #2338, @jcuquemelle; #2285, @avflor; #2340, @pogil; #2237, #2226, #2243, #2272, #2286, @dbczumar; #2281, @renaudhager; #2246, @avaucher; #2258, @lorenzwalthert; #2261, @smith-kyle; 2352, @dbczumar)

## 1.5.0 (2019-12-19)

MLflow 1.5.0 includes several major features and improvements:

New Model Flavors and Flavor Updates:

- New support for a LightGBM flavor (#2136, @harupy)
- New support for a XGBoost flavor (#2124, @harupy)
- New support for a Gluon flavor and autologging (#1973, @cosmincatalin)
- Runs automatically created by `mlflow.tensorflow.autolog()` and `mlflow.keras.autolog()` (#2088) are now automatically ended after training and/or exporting your model. See the [`docs`](https://mlflow.org/docs/latest/tracking.html#automatic-logging-from-tensorflow-and-keras-experimental) for more details (#2094, @juntai-zheng)

More features and improvements:

- When using the `mlflow server` CLI command, you can now expose metrics on `/metrics` for Prometheus via the optional --activate-parameter argument (#2097, @t-henri)
- The `mlflow ui` CLI command now has a `--host`/`-h` option to specify user-input IPs to bind to (#2176, @gandroz)
- MLflow now supports pulling Git submodules while using MLflow Projects (#2103, @badc0re)
- New `mlflow models prepare-env` command to do any preparation necessary to initialize an environment. This allows distinguishing configuration and user errors during predict/serve time (#2040, @aarondav)
- TensorFlow.Keras and Keras parameters are now logged by `autolog()` (#2119, @juntai-zheng)
- MLflow `log_params()` will recognize Spark ML params as keys and will now extract only the name attribute (#2064, @tomasatdatabricks)
- Exposes `mlflow.tracking.is_tracking_uri_set()` (#2026, @fhoering)
- The artifact image viewer now displays "Loading..." when it is loading an image (#1958, @harupy)
- The artifact image view now supports animated GIFs (#2070, @harupy)
- Adds ability to mount volumes and specify environment variables when using mlflow with docker (#1994, @nlml)
- Adds run context for detecting job information when using MLflow tracking APIs within Databricks Jobs. The following job types are supported: notebook jobs, Python Task jobs (#2205, @dbczumar)
- Performance improvement when searching for runs (#2030, #2059, @jcuquemelle; #2195, @rom1504)

Bug fixes and documentation updates:

- Fixed handling of empty directories in FS based artifact repositories (#1891, @tomasatdatabricks)
- Fixed `mlflow.keras.save_model()` usage with DBFS (#2216, @andychow-db)
- Fixed several build issues for the Docker image (#2107, @jimthompson5802)
- Fixed `mlflow_list_artifacts()` (R package) (#2200, @lorenzwalthert)
- Entrypoint commands of Kubernetes jobs are now shell-escaped (#2160, @zanitete)
- Fixed project run Conda path issue (#2147, @Zangr)
- Fixed spark model load from model repository (#2175, @tomasatdatabricks)
- Stripped "dev" suffix from PySpark versions (#2137, @dbczumar)
- Fixed note editor on the experiment page (#2054, @harupy)
- Fixed `models serve`, `models predict` CLI commands against models:/ URIs (#2067, @smurching)
- Don't unconditionally format values as metrics in generic HtmlTableView component (#2068, @smurching)
- Fixed remote execution from Windows using posixpath (#1996, @aestene)
- Add XGBoost and LightGBM examples (#2186, @harupy)
- Add note about active run instantiation side effect in fluent APIs (#2197, @andychow-db)
- The tutorial page has been refactored to be be a 'Tutorials and Examples' page (#2182, @juntai-zheng)
- Doc enhancements for XGBoost and LightGBM flavors (#2170, @harupy)
- Add doc for XGBoost flavor (#2167, @harupy)
- Updated `active_run()` docs to clarify it cannot be used accessing current run data (#2138, @juntai-zheng)
- Document models:/ scheme for URI for load_model methods (#2128, @stbof)
- Added an example using Prophet via pyfunc (#2043, @dr3s)
- Added and updated some screenshots and explicit steps for the model registry (#2086, @stbof)

Small bug fixes and doc updates (#2142, #2121, #2105, #2069, #2083, #2061, #2022, #2036, #1972, #2034, #1998, #1959, @harupy; #2202, @t-henri; #2085, @stbof; #2098, @AdamBarnhard; #2180, #2109, #1977, #2039, #2062, @smurching; #2013, @aestene; #2146, @joelcthomas; #2161, #2120, #2100, #2095, #2088, #2076, #2057, @juntai-zheng; #2077, #2058, #2027, @sueann; #2149, @zanitete; #2204, #2188, @andychow-db; #2110, #2053, @jdlesage; #2003, #1953, #2004, @Djailla; #2074, @nlml; #2116, @Silas-Asamoah; #1104, @jimthompson5802; #2072, @cclauss; #2221, #2207, #2157, #2132, #2114, #2063, #2065, #2055, @dbczumar; #2033, @cthoyt; #2048, @philip-khor; #2002, @jspoorta; #2000, @christang; #2078, @dennyglee; #1986, @vguerra; #2020, @dependabot[bot])

## 1.4.0 (2019-10-30)

MLflow 1.4.0 includes several major features:

- Model Registry (Beta). Adds an experimental model registry feature, where you can manage, version, and keep lineage of your production models. (#1943, @mparkhe, @Zangr, @sueann, @dbczumar, @smurching, @gioa, @clemens-db, @pogil, @mateiz; #1988, #1989, #1995, #2021, @mparkhe; #1983, #1982, #1967, @dbczumar)
- TensorFlow updates

  - MLflow Keras model saving, loading, and logging has been updated to be compatible with TensorFlow 2.0. (#1927, @juntai-zheng)
  - Autologging for `tf.estimator` and `tf.keras` models has been updated to be compatible with TensorFlow 2.0. The same functionalities of autologging in TensorFlow 1.x are available in TensorFlow 2.0, namely when fitting `tf.keras` models and when exporting saved `tf.estimator` models. (#1910, @juntai-zheng)
  - Examples and READMEs for both TensorFlow 1.X and TensorFlow 2.0 have been added to `mlflow/examples/tensorflow`. (#1946, @juntai-zheng)

More features and improvements:

- [API] Add functions `get_run`, `get_experiment`, `get_experiment_by_name` to the fluent API (#1923, @fhoering)
- [UI] Use Plotly as artifact image viewer, which allows zooming and panning (#1934, @harupy)
- [UI] Support deleting tags from the run details page (#1933, @harupy)
- [UI] Enable scrolling to zoom in metric and run comparison plots (#1929, @harupy)
- [Artifacts] Add support of viewfs URIs for HDFS federation for artifacts (#1947, @t-henri)
- [Models] Spark UDFs can now be called with struct input if the underlying spark implementation supports it. The data is passed as a pandas DataFrame with column names matching those in the struct. (#1882, @tomasatdatabricks)
- [Models] Spark models will now load faster from DFS by skipping unnecessary copies (#2008, @tomasatdatabricks)

Bug fixes and documentation updates:

- [Projects] Make detection of `MLproject` files case-insensitive (#1981, @smurching)
- [UI] Fix a bug where viewing metrics containing forward-slashes in the name would break the MLflow UI (#1968, @smurching)
- [CLI] `models serve` command now works in Windows (#1949, @rboyes)
- [Scoring] Fix a dependency installation bug in Java MLflow model scoring server (#1913, @smurching)

Small bug fixes and doc updates (#1932, #1935, @harupy; #1907, @marnixkoops; #1911, @HackyRoot; #1931, @jmcarp; #2007, @deniskovalenko; #1966, #1955, #1952, @Djailla; #1915, @sueann; #1978, #1894, @smurching; #1940, #1900, #1904, @mparkhe; #1914, @jerrygb; #1857, @mengxr; #2009, @dbczumar)

## 1.3 (2019-09-30)

MLflow 1.3.0 includes several major features and improvements:

Features:

- The Python client now supports logging & loading models using TensorFlow 2.0 (#1872, @juntai-zheng)
- Significant performance improvements when fetching runs and experiments in MLflow servers that use SQL database-backed storage (#1767, #1878, #1805 @dbczumar)
- New `GetExperimentByName` REST API endpoint, used in the Python client to speed up `set_experiment` and `get_experiment_by_name` (#1775, @smurching)
- New `mlflow.delete_run`, `mlflow.delete_experiment` fluent APIs in the Python client(#1396, @MerelTheisenQB)
- New CLI command (`mlflow experiments csv`) to export runs of an experiment into a CSV (#1705, @jdlesage)
- Directories can now be logged as artifacts via `mlflow.log_artifact` in the Python fluent API (#1697, @apurva-koti)
- HTML and geojson artifacts are now rendered in the run UI (#1838, @sim-san; #1803, @spadarian)
- Keras autologging support for `fit_generator` Keras API (#1757, @charnger)
- MLflow models packaged as docker containers can be executed via Google Cloud Run (#1778, @ngallot)
- Artifact storage configurations are propagated to containers when executing docker-based MLflow projects locally (#1621, @nlaille)
- The Python, Java, R clients and UI now retry HTTP requests on 429 (Too Many Requests) errors (#1846, #1851, #1858, #1859 @tomasatdatabricks; #1847, @smurching)

Bug fixes and documentation updates:

- The R `mlflow_list_artifact` API no longer throws when listing artifacts for an empty run (#1862, @smurching)
- Fixed a bug preventing running the MLflow server against an MS SQL database (#1758, @sifanLV)
- MLmodel files (artifacts) now correctly display in the run UI (#1819, @ankitmathur-db)
- The Python `mlflow.start_run` API now throws when resuming a run whose experiment ID differs from the
  active experiment ID set via `mlflow.set_experiment` (#1820, @mcminnra).
- `MlflowClient.log_metric` now logs metric timestamps with millisecond (as opposed to second) resolution (#1804, @ustcscgyer)
- Fixed bugs when listing (#1800, @ahutterTA) and downloading (#1890, @jdlesage) artifacts stored in HDFS.
- Fixed a bug preventing Kubernetes Projects from pushing to private Docker repositories (#1788, @dbczumar)
- Fixed a bug preventing deploying Spark models to AzureML (#1769, @Ben-Epstein)
- Fixed experiment id resolution in projects (#1715, @drewmcdonald)
- Updated parallel coordinates plot to show all fields available in compared runs (#1753, @mateiz)
- Streamlined docs for getting started with hosted MLflow (#1834, #1785, #1860 @smurching)

Small bug fixes and doc updates (#1848, @pingsutw; #1868, @iver56; #1787, @apurvakoti; #1741, #1737, @apurva-koti; #1876, #1861, #1852, #1801, #1754, #1726, #1780, #1807 @smurching; #1859, #1858, #1851, @tomasatdatabricks; #1841, @ankitmathur-db; #1744, #1746, #1751, @mateiz; #1821, #1730, @dbczumar; #1727, cfmcgrady; #1716, @axsaucedo; #1714, @fhoering; #1405, @ancasarb; #1502, @jimthompson5802; #1720, jke-zq; #1871, @mehdi254; #1782, @stbof)

## 1.2 (2019-08-09)

MLflow 1.2 includes the following major features and improvements:

- Experiments now have editable tags and descriptions (#1630, #1632, #1678, @ankitmathur-db)
- Search latency has been significantly reduced in the SQLAlchemyStore (#1660, @t-henri)

**More features and improvements**

- Backend stores now support run tag values up to 5000 characters in length. Some store implementations may support longer tag values (#1687, @ankitmathur-db)
- Gunicorn options can now be configured for the `mlflow models serve` CLI with the `GUNICORN_CMD_ARGS` environment variable (#1557, @LarsDu)
- Jsonnet artifacts can now be previewed in the UI (#1683, @ankitmathur-db)
- Adds an optional `python_version` argument to `mlflow_install` for specifying the Python version (e.g. "3.5") to use within the conda environment created for installing the MLflow CLI. If `python_version` is unspecified, `mlflow_install` defaults to using Python 3.6. (#1722, @smurching)

**Bug fixes and documentation updates**

- [Tracking] The Autologging feature is now more resilient to tracking errors (#1690, @apurva-koti)
- [Tracking] The `runs` field in in the `GetExperiment.Response` proto has been deprecated & will be removed in MLflow 2.0. Please use the `Search Runs` API for fetching runs instead (#1647, @dbczumar)
- [Projects] Fixed a bug that prevented docker-based MLflow Projects from logging artifacts to the `LocalArtifactRepository` (#1450, @nlaille)
- [Projects] Running MLflow projects with the `--no-conda` flag in R no longer requires Anaconda to be installed (#1650, @spadarian)
- [Models/Scoring] Fixed a bug that prevented Spark UDFs from being loaded on Databricks (#1658, @smurching)
- [UI] AJAX requests made by the MLflow Server Frontend now specify correct MIME-Types (#1679, @ynotzort)
- [UI] Previews now render correctly for artifacts with uppercase file extensions (e.g., `.JSON`, `.YAML`) (#1664, @ankitmathur-db)
- [UI] Fixed a bug that caused search API errors to surface a Niagara Falls page (#1681, @dbczumar)
- [Installation] MLflow dependencies are now selected properly based on the target installation platform (#1643, @akshaya-a)
- [UI] Fixed a bug where the "load more" button in the experiment view did not appear on browsers in Windows (#1718, @Zangr)

Small bug fixes and doc updates (#1663, #1719, @dbczumar; #1693, @max-allen-db; #1695, #1659, @smurching; #1675, @jdlesage; #1699, @ankitmathur-db; #1696, @aarondav; #1710, #1700, #1656, @apurva-koti)

## 1.1 (2019-07-22)

MLflow 1.1 includes several major features and improvements:

In MLflow Tracking:

- Experimental support for autologging from Tensorflow and Keras. Using `mlflow.tensorflow.autolog()` will enable automatic logging of metrics and optimizer parameters from TensorFlow to MLflow. The feature will work with TensorFlow versions `1.12 <= v < 2.0`. (#1520, #1601, @apurva-koti)
- Parallel coordinates plot in the MLflow compare run UI. Adds out of the box support for a parallel coordinates plot. The plot allows users to observe relationships between a n-dimensional set of parameters to metrics. It visualizes all runs as lines that are color-coded based on the value of a metric (e.g. accuracy), and shows what parameter values each run took on. (#1497, @Zangr)
- Pandas based search API. Adds the ability to return the results of a search as a pandas dataframe using the new `mlflow.search_runs` API. (#1483, #1548, @max-allen-db)
- Java fluent API. Adds a new set of APIs to create and log to MLflow runs. This API contrasts with the existing low level `MlflowClient` API which simply wraps the REST APIs. The new fluent API allows you to create and log runs similar to how you would using the Python fluent API. (#1508, @andrewmchen)
- Run tags improvements. Adds the ability to add and edit tags from the run view UI, delete tags from the API, and view tags in the experiment search view. (#1400, #1426, @Zangr; #1548, #1558, @ankitmathur-db)
- Search API improvements. Adds order by and pagination to the search API. Pagination allows you to read a large set of runs in small page sized chunks. This allows clients and backend implementations to handle an unbounded set of runs in a scalable manner. (#1444, @sueann; #1437, #1455, #1482, #1485, #1542, @aarondav; #1567, @max-allen-db; #1217, @mparkhe)
- Windows support for running the MLflow tracking server and UI. (#1080, @akshaya-a)

In MLflow Projects:

- Experimental support to run Docker based MLprojects in Kubernetes. Adds the first fully open source remote execution backend for MLflow projects. With this, you can leverage elastic compute resources managed by kubernetes for their ML training purposes. For example, you can run grid search over a set of hyperparameters by running several instances of an MLproject in parallel. (#1181, @marcusrehm, @tomasatdatabricks, @andrewmchen; #1566, @stbof, @dbczumar; #1574 @dbczumar)

**More features and improvements**

In MLflow Tracking:

- Paginated "load more" and backend sorting for experiment search view UI. This change allows the UI to scalably display the sorted runs from large experiments. (#1564, @Zangr)
- Search results are encoded in the URL. This allows you to share searches through their URL and to deep link to them. (#1416, @apurva-koti)
- Ability to serve MLflow UI behind `jupyter-server-proxy` or outside of the root path `/`. Previous to MLflow 1.1, the UI could only be hosted on `/` since the Javascript makes requests directly to `/ajax-api/...`. With this patch, MLflow will make requests to `ajax-api/...` or a path relative to where the HTML is being served. (#1413, @xhochy)

In MLflow Models:

- Update `mlflow.spark.log_model()` to accept descendants of pyspark.Model (#1519, @ankitmathur-db)
- Support for saving custom Keras models with `custom_objects`. This field is semantically equivalent to custom_objects parameter of `keras.models.load_model()` function (#1525, @ankitmathur-db)
- New more performant split orient based input format for pyfunc scoring server (#1479, @lennon310)
- Ability to specify gunicorn server options for pyfunc scoring server built with `mlflow models build-docker`. #1428, @lennon310)

**Bug fixes and documentation updates**

- [Tracking] Fix database migration for MySQL. `mlflow db upgrade` should now work for MySQL backends. (#1404, @sueann)
- [Tracking] Make CLI `mlflow server` and `mlflow ui` commands to work with SQLAlchemy URIs that specify a database driver. (#1411, @sueann)
- [Tracking] Fix usability bugs related to FTP artifact repository. (#1398, @kafendt; #1421, @nlaille)
- [Tracking] Return appropriate HTTP status codes for MLflowException (#1434, @max-allen-db)
- [Tracking] Fix sorting by user ID in the experiment search view. (#1401, @andrewmchen)
- [Tracking] Allow calling log_metric with NaNs and infs. (#1573, @tomasatdatabricks)
- [Tracking] Fixes an infinite loop in downloading artifacts logged via dbfs and retrieved via S3. (#1605, @sueann)
- [Projects] Docker projects should preserve directory structure (#1436, @ahutterTA)
- [Projects] Fix conda activation for newer versions of conda. (#1576, @avinashraghuthu, @smurching)
- [Models] Allow you to log Tensorflow keras models from the `tf.keras` module. (#1546, @tomasatdatabricks)

Small bug fixes and doc updates (#1463, @mateiz; #1641, #1622, #1418, @sueann; #1607, #1568, #1536, #1478, #1406, #1408, @smurching; #1504, @LizaShak; #1490, @acroz; #1633, #1631, #1603, #1589, #1569, #1526, #1446, #1438, @apurva-koti; #1456, @Taur1ne; #1547, #1495, @aarondav; #1610, #1600, #1492, #1493, #1447, @tomasatdatabricks; #1430, @javierluraschi; #1424, @nathansuh; #1488, @henningsway; #1590, #1427, @Zangr; #1629, #1614, #1574, #1521, #1522, @dbczumar; #1577, #1514, @ankitmathur-db; #1588, #1566, @stbof; #1575, #1599, @max-allen-db; #1592, @abaveja313; #1606, @andrewmchen)

## 1.0 (2019-06-03)

MLflow 1.0 includes many significant features and improvements. From this version, MLflow is no longer beta, and all APIs except those marked as experimental are intended to be stable until the next major version. As such, this release includes a number of breaking changes.

Major features, improvements, and breaking changes

- Support for recording, querying, and visualizing metrics along a new "step" axis (x coordinate), providing increased flexibility for examining model performance relative to training progress. For example, you can now record performance metrics as a function of the number of training iterations or epochs. MLflow 1.0's enhanced metrics UI enables you to visualize the change in a metric's value as a function of its step, augmenting MLflow's existing UI for plotting a metric's value as a function of wall-clock time. (#1202, #1237, @dbczumar; #1132, #1142, #1143, @smurching; #1211, #1225, @Zangr; #1372, @stbof)
- Search improvements. MLflow 1.0 includes additional support in both the API and UI for searching runs within a single experiment or a group of experiments. The search filter API supports a simplified version of the `SQL WHERE` clause. In addition to searching using run's metrics and params, the API has been enhanced to support a subset of run attributes as well as user and [system tags](https://mlflow.org/docs/latest/tracking.html#system-tags). For details see [Search syntax](https://mlflow.org/docs/latest/search-syntax.html#syntax) and [examples for programmatically searching runs](https://mlflow.org/docs/latest/search-syntax.html#programmatically-searching-runs). (#1245, #1272, #1323, #1326, @mparkhe; #1052, @Zangr; #1363, @aarondav)
- Logging metrics in batches. MLflow 1.0 now has a `runs/log-batch` REST API endpoint for logging multiple metrics, params, and tags in a single API request. The endpoint useful for performant logging of multiple metrics at the end of a model training epoch (see [example](https://github.com/mlflow/mlflow/blob/bb8c7602dcb6a3a8786301fe6b98f01e8d3f288d/examples/hyperparam/search_hyperopt.py#L161)), or logging of many input model parameters at the start of training. You can call this batched-logging endpoint from Python (`mlflow.log_metrics`, `mlflow.log_params`, `mlflow.set_tags`), R (`mlflow_log_batch`), and Java (`MlflowClient.logBatch`). (#1214, @dbczumar; see 0.9.1 and 0.9.0 for other changes)
- Windows support for MLflow Tracking. The Tracking portion of the MLflow client is now supported on Windows. (#1171, @eedeleon, @tomasatdatabricks)
- HDFS support for artifacts. Hadoop artifact repository with Kerberos authorization support was added, so you can use HDFS to log and retrieve models and other artifacts. (#1011, @jaroslawk)
- CLI command to build Docker images for serving. Added an `mlflow models build-docker` CLI command for building a Docker image capable of serving an MLflow model. The model is served at port 8080 within the container by default. Note that this API is experimental and does not guarantee that the arguments nor format of the Docker container will remain the same. (#1329, @smurching, @tomasatdatabricks)
- New `onnx` model flavor for saving, loading, and evaluating ONNX models with MLflow. ONNX flavor APIs are available in the `mlflow.onnx` module. (#1127, @avflor, @dbczumar; #1388, #1389, @dbczumar)
- Major breaking changes:

  - Some of the breaking changes involve database schema changes in the SQLAlchemy tracking store. If your database instance's schema is not up-to-date, MLflow will issue an error at the start-up of `mlflow server` or `mlflow ui`. To migrate an existing database to the newest schema, you can use the `mlflow db upgrade` CLI command. (#1155, #1371, @smurching; #1360, @aarondav)
  - [Installation] The MLflow Python package no longer depends on `scikit-learn`, `mleap`, or `boto3`. If you want to use the `scikit-learn` support, the `MLeap` support, or `s3` artifact repository / `sagemaker` support, you will have to install these respective dependencies explicitly. (#1223, @aarondav)
  - [Artifacts] In the Models API, an artifact's location is now represented as a URI. See the [documentation](https://mlflow.org/docs/latest/tracking.html#artifact-locations) for the list of accepted URIs. (#1190, #1254, @dbczumar; #1174, @dbczumar, @sueann; #1206, @tomasatdatabricks; #1253, @stbof)

    - The affected methods are:

      - Python: `<model-type>.load_model`, `azureml.build_image`, `sagemaker.deploy`, `sagemaker.run_local`, `pyfunc._load_model_env`, `pyfunc.load_pyfunc`, and `pyfunc.spark_udf`
      - R: `mlflow_load_model`, `mlflow_rfunc_predict`, `mlflow_rfunc_serve`
      - CLI: `mlflow models serve`, `mlflow models predict`, `mlflow sagemaker`, `mlflow azureml` (with the new `--model-uri` option)

    - To allow referring to artifacts in the context of a run, MLflow introduces a new URI scheme of the form `runs:/<run_id>/relative/path/to/artifact`. (#1169, #1175, @sueann)

  - [CLI] `mlflow pyfunc` and `mlflow rfunc` commands have been unified as `mlflow models` (#1257, @tomasatdatabricks; #1321, @dbczumar)
  - [CLI] `mlflow artifacts download`, `mlflow artifacts download-from-uri` and `mlflow download` commands have been consolidated into `mlflow artifacts download` (#1233, @sueann)
  - [Runs] Expose `RunData` fields (`metrics`, `params`, `tags`) as dictionaries. Note that the `mlflow.entities.RunData` constructor still accepts lists of `metric`/`param`/`tag` entities. (#1078, @smurching)
  - [Runs] Rename `run_uuid` to `run_id` in Python, Java, and REST API. Where necessary, MLflow will continue to accept `run_uuid` until MLflow 1.1. (#1187, @aarondav)

Other breaking changes

CLI:

- The `--file-store` option is deprecated in `mlflow server` and `mlflow ui` commands. (#1196, @smurching)
- The `--host` and `--gunicorn-opts` options are removed in the `mlflow ui` command. (#1267, @aarondav)
- Arguments to `mlflow experiments` subcommands, notably `--experiment-name` and `--experiment-id` are now options (#1235, @sueann)
- `mlflow sagemaker list-flavors` has been removed (#1233, @sueann)

Tracking:

- The `user` property of `Run`s has been moved to tags (similarly, the `run_name`, `source_type`, `source_name` properties were moved to tags in 0.9.0). (#1230, @acroz; #1275, #1276, @aarondav)
- In R, the return values of experiment CRUD APIs have been updated to more closely match the REST API. In particular, `mlflow_create_experiment` now returns a string experiment ID instead of an experiment, and the other APIs return NULL. (#1246, @smurching)
- `RunInfo.status`'s type is now string. (#1264, @mparkhe)
- Remove deprecated `RunInfo` properties from `start_run`. (#1220, @aarondav)
- As deprecated in 0.9.1 and before, the `RunInfo` fields `run_name`, `source_name`, `source_version`, `source_type`, and `entry_point_name` and the `SearchRuns` field `anded_expressions` have been removed from the REST API and Python, Java, and R tracking client APIs. They are still available as tags, documented in the REST API documentation. (#1188, @aarondav)

Models and deployment:

- In Python, require arguments as keywords in `log_model`, `save_model` and `add_to_model` methods in the `tensorflow` and `mleap` modules to avoid breaking changes in the future (#1226, @sueann)
- Remove the unsupported `jars` argument from ``spark.log_model` in Python (#1222, @sueann)
- Introduce `pyfunc.load_model` to be consistent with other Models modules. `pyfunc.load_pyfunc` will be deprecated in the near future. (#1222, @sueann)
- Rename `dst_path` parameter in `pyfunc.save_model` to `path` (#1221, @aarondav)
- R flavors refactor (#1299, @kevinykuo)

  - `mlflow_predict()` has been added in favor of `mlflow_predict_model()` and `mlflow_predict_flavor()` which have been removed.
  - `mlflow_save_model()` is now a generic and `mlflow_save_flavor()` is no longer needed and has been removed.
  - `mlflow_predict()` takes `...` to pass to underlying predict methods.
  - `mlflow_load_flavor()` now has the signature `function(flavor, model_path)` and flavor authors should implement `mlflow_load_flavor.mlflow_flavor_{FLAVORNAME}`. The flavor argument is inferred from the inputs of user-facing `mlflow_load_model()` and does not need to be explicitly provided by the user.

Projects:

- Remove and rename some `projects.run` parameters for generality and consistency. (#1222, @sueann)
- In R, the `mlflow_run` API for running MLflow projects has been modified to more closely reflect the Python `mlflow.run` API. In particular, the order of the `uri` and `entry_point` arguments has been reversed and the `param_list` argument has been renamed to `parameters`. (#1265, @smurching)

R:

- Remove `mlflow_snapshot` and `mlflow_restore_snapshot` APIs. Also, the `r_dependencies` argument used to specify the path to a packrat r-dependencies.txt file has been removed from all APIs. (#1263, @smurching)
- The `mlflow_cli` and `crate` APIs are now private. (#1246, @smurching)

Environment variables:

- Prefix environment variables with "MLFLOW\_" (#1268, @aarondav). Affected variables are:

  - [Tracking] `_MLFLOW_SERVER_FILE_STORE`, `_MLFLOW_SERVER_ARTIFACT_ROOT`, `_MLFLOW_STATIC_PREFIX`
  - [SageMaker] `MLFLOW_SAGEMAKER_DEPLOY_IMG_URL`, `MLFLOW_DEPLOYMENT_FLAVOR_NAME`
  - [Scoring] `MLFLOW_SCORING_SERVER_MIN_THREADS`, `MLFLOW_SCORING_SERVER_MAX_THREADS`

More features and improvements

- [Tracking] Non-default driver support for SQLAlchemy backends: `db+driver` is now a valid tracking backend URI scheme (#1297, @drewmcdonald; #1374, @mparkhe)
- [Tracking] Validate backend store URI before starting tracking server (#1218, @luke-zhu, @sueann)
- [Tracking] Add `GetMetricHistory` client API in Python and Java corresponding to the REST API. (#1178, @smurching)
- [Tracking] Add `view_type` argument to `MlflowClient.list_experiments()` in Python. (#1212, @smurching)
- [Tracking] Dictionary values provided to `mlflow.log_params` and `mlflow.set_tags` in Python can now be non-string types (e.g., numbers), and they are automatically converted to strings. (#1364, @aarondav)
- [Tracking] R API additions to be at parity with REST API and Python (#1122, @kevinykuo)
- [Tracking] Limit number of results returned from `SearchRuns` API and UI for faster load (#1125, @mparkhe; #1154, @andrewmchen)
- [Artifacts] To avoid having many copies of large model files in serving, `ArtifactRepository.download_artifacts` no longer copies local artifacts (#1307, @andrewmchen; #1383, @dbczumar)
- [Artifacts/Projects] Support GCS in download utilities. `gs://bucket/path` files are now supported by the `mlflow artifacts download` CLI command and as parameters of type `path` in MLProject files. (#1168, @drewmcdonald)
- [Models] All Python models exported by MLflow now declare `mlflow` as a dependency by default. In addition, we introduce a flag `--install-mlflow` users can pass to `mlflow models serve` and `mlflow models predict` methods to force installation of the latest version of MLflow into the model's environment. (#1308, @tomasatdatabricks)
- [Models] Update model flavors to lazily import dependencies in Python. Modules that define Model flavors now import extra dependencies such as `tensorflow`, `scikit-learn`, and `pytorch` inside individual _methods_, ensuring that these modules can be imported and explored even if the dependencies have not been installed on your system. Also, the `DEFAULT_CONDA_ENVIRONMENT` module variable has been replaced with a `get_default_conda_env()` function for each flavor. (#1238, @dbczumar)
- [Models] It is now possible to pass extra arguments to `mlflow.keras.load_model` that will be passed through to `keras.load_model`. (#1330, @yorickvP)
- [Serving] For better performance, switch to `gunicorn` for serving Python models. This does not change the user interface. (#1322, @tomasatdatabricks)
- [Deployment] For SageMaker, use the uniquely-generated model name as the S3 bucket prefix instead of requiring one. (#1183, @dbczumar)
- [REST API] Add support for API paths without the `preview` component. The `preview` paths will be deprecated in a future version of MLflow. (#1236, @mparkhe)

Bug fixes and documentation updates

- [Tracking] Log metric timestamps in milliseconds by default (#1177, @smurching; #1333, @dbczumar)
- [Tracking] Fix bug when deserializing integer experiment ID for runs in `SQLAlchemyStore` (#1167, @smurching)
- [Tracking] Ensure unique constraint names in MLflow tracking database (#1292, @smurching)
- [Tracking] Fix base64 encoding for basic auth in R tracking client (#1126, @freefrag)
- [Tracking] Correctly handle `file:` URIs for the `-—backend-store-uri` option in `mlflow server` and `mlflow ui` CLI commands (#1171, @eedeleon, @tomasatdatabricks)
- [Artifacts] Update artifact repository download methods to return absolute paths (#1179, @dbczumar)
- [Artifacts] Make FileStore respect the default artifact location (#1332, @dbczumar)
- [Artifacts] Fix `log_artifact` failures due to existing directory on FTP server (#1327, @kafendt)
- [Artifacts] Fix GCS artifact logging of subdirectories (#1285, @jason-huling)
- [Projects] Fix bug not sharing `SQLite` database file with Docker container (#1347, @tomasatdatabricks; #1375, @aarondav)
- [Java] Mark `sendPost` and `sendGet` as experimental (#1186, @aarondav)
- [Python/CLI] Mark `azureml.build_image` as experimental (#1222, #1233 @sueann)
- [Docs] Document public MLflow environment variables (#1343, @aarondav)
- [Docs] Document MLflow system tags for runs (#1342, @aarondav)
- [Docs] Autogenerate CLI documentation to include subcommands and descriptions (#1231, @sueann)
- [Docs] Update run selection description in `mlflow_get_run` in R documentation (#1258, @dbczumar)
- [Examples] Update examples to reflect API changes (#1361, @tomasatdatabricks; #1367, @mparkhe)

Small bug fixes and doc updates (#1359, #1350, #1331, #1301, #1270, #1271, #1180, #1144, #1135, #1131, #1358, #1369, #1368, #1387, @aarondav; #1373, @akarloff; #1287, #1344, #1309, @stbof; #1312, @hchiuzhuo; #1348, #1349, #1294, #1227, #1384, @tomasatdatabricks; #1345, @withsmilo; #1316, @ancasarb; #1313, #1310, #1305, #1289, #1256, #1124, #1097, #1162, #1163, #1137, #1351, @smurching; #1319, #1244, #1224, #1195, #1194, #1328, @dbczumar; #1213, #1200, @Kublai-Jing; #1304, #1320, @andrewmchen; #1311, @Zangr; #1306, #1293, #1147, @mateiz; #1303, @gliptak; #1261, #1192, @eedeleon; #1273, #1259, @kevinykuo; #1277, #1247, #1243, #1182, #1376, @mparkhe; #1210, @vgod-dbx; #1199, @ashtuchkin; #1176, #1138, #1365, @sueann; #1157, @cclauss; #1156, @clemens-db; #1152, @pogil; #1146, @srowen; #875, #1251, @jimthompson5802)

## 0.9.1 (2019-04-21)

MLflow 0.9.1 is a patch release on top of 0.9.0 containing mostly bug fixes and internal improvements. We have also included a one breaking API change in preparation for additions in MLflow 1.0 and later. This release also includes significant improvements to the Search API.

Breaking changes:

- [Tracking] Generalized experiment_id to string (from a long) to be more permissive of different ID types in different backend stores. While breaking for the REST API, this change is backwards compatible for python and R clients. (#1067, #1034 @eedeleon)

More features and improvements:

- [Search/API] Moving search filters into a query string based syntax, with Java client, Python client, and UI support. This also improves quote, period, and special character handling in query strings and adds the ability to search on tags in filter string. (#1042, #1055, #1063, #1068, #1099, #1106 @mparkhe; #1025 @andrewmchen; #1060 @smurching)
- [Tracking] Limits and validations to batch-logging APIs in OSS server (#958 @smurching)
- [Tracking/Java] Java client API for batch-logging (#1081 @mparkhe)
- [Tracking] Improved consistency of handling multiple metric values per timestamp across tracking stores (#972, #999 @dbczumar)

Bug fixes and documentation updates:

- [Tracking/Python] Reintroduces the parent_run_id argument to MlflowClient.create_run. This API is planned for removal in MLflow 1.0 (#1137 @smurching)
- [Tracking/Python] Provide default implementations of AbstractStore log methods (#1051 @acroz)
- [R] (Released on CRAN as MLflow 0.9.0.1) Small bug fixes with R (#1123 @smurching; #1045, #1017, #1019, #1039, #1048, #1098, #1101, #1107, #1108, #1119 @tomasatdatabricks)

Small bug fixes and doc updates (#1024, #1029 @bayethiernodiop; #1075 @avflor; #968, #1010, #1070, #1091, #1092 @smurching; #1004, #1085 @dbczumar; #1033, #1046 @sueann; #1053 @tomasatdatabricks; #987 @hanyucui; #935, #941 @jimthompson5802; #963 @amilbourne; #1016 @andrewmchen; #991 @jaroslawk; #1007 @mparkhe)

## 0.9.0.1 (2019-04-09)

Bugfix release (PyPI only) with the following changes:

- Rebuilt MLflow JS assets to fix an issue where form input was broken in MLflow 0.9.0 (identified
  in #1056, #1113 by @shu-yusa, @timothyjlaurent)

  0.9.0 (2019-03-13)

Major features:

- Support for running MLflow Projects in Docker containers. This allows you to include non-Python dependencies in their project environments and provides stronger isolation when running projects. See the [Projects documentation](https://mlflow.org/docs/latest/projects.html) for more information. (#555, @marcusrehm; #819, @mparkhe; #970, @dbczumar)
- Database stores for the MLflow Tracking Server. Support for a scalable and performant backend store was one of the top community requests. This feature enables you to connect to local or remote SQLAlchemy-compatible databases (currently supported flavors include MySQL, PostgreSQL, SQLite, and MS SQL) and is compatible with file backed store. See the [Tracking Store documentation](https://mlflow.org/docs/latest/tracking.html#storage) for more information. (#756, @AndersonReyes; #800, #844, #847, #848, #860, #868, #975, @mparkhe; #980, @dbczumar)
- Simplified custom Python model packaging. You can easily include custom preprocessing and postprocessing logic, as well as data dependencies in models with the `python_function` flavor using updated `mlflow.pyfunc` Python APIs. For more information, see the [Custom Python Models documentation](https://mlflow.org/docs/latest/models.html#custom-python-models). (#791, #792, #793, #830, #910, @dbczumar)
- Plugin systems allowing third party libraries to extend MLflow functionality. The [proposal document](https://gist.github.com/zblz/9e337a55a7ba73314890be68370fa69a) gives the full detail of the three main changes:

  - You can register additional providers of tracking stores using the `mlflow.tracking_store` entrypoint. (#881, @zblz)
  - You can register additional providers of artifact repositories using the `mlflow.artifact_repository` entrypoint. (#882, @mociarain)
  - The logic generating run metadata from the run context (e.g. `source_name`, `source_version`) has been refactored into an extendable system of run context providers. Plugins can register additional providers using the `mlflow.run_context_provider` entrypoint, which add to or overwrite tags set by the base library. (#913, #926, #930, #978, @acroz)

- Support for HTTP authentication to the Tracking Server in the R client. Now you can connect to secure Tracking Servers using credentials set in environment variables, or provide custom plugins for setting the credentials. As an example, this release contains a Databricks plugin that can detect existing Databricks credentials to allow you to connect to the Databricks Tracking Server. (#938, #959, #992, @tomasatdatabricks)

Breaking changes:

- [Scoring] The `pyfunc` scoring server now expects requests with the `application/json` content type to contain json-serialized pandas dataframes in the split format, rather than the records format. See the [documentation on deployment](https://mlflow.org/docs/latest/models.html#deploy-a-python-function-model-as-a-local-rest-api-endpoint) for more detail. (#960, @dbczumar) Also, when reading the pandas dataframes from JSON, the scoring server no longer automatically infers data types as it can result in unintentional conversion of data types (#916, @mparkhe).
- [API] Remove `GetMetric` & `GetParam` from the REST API as they are subsumed by `GetRun`. (#879, @aarondav)

More features and improvements:

- [UI] Add a button for downloading artifacts (#967, @mateiz)
- [CLI] Add CLI commands for runs: now you can `list`, `delete`, `restore`, and `describe` runs through the CLI (#720, @DorIndivo)
- [CLI] The `run` command now can take `--experiment-name` as an argument, as an alternative to the `--experiment-id` argument. You can also choose to set the `_EXPERIMENT_NAME_ENV_VAR` environment variable instead of passing in the value explicitly. (#889, #894, @mparkhe)
- [Examples] Add Image classification example with Keras. (#743, @tomasatdatabricks )
- [Artifacts] Add `get_artifact_uri()` and `_download_artifact_from_uri` convenience functions (#779)
- [Artifacts] Allow writing Spark models directly to the target artifact store when possible (#808, @smurching)
- [Models] PyTorch model persistence improvements to allow persisting definitions and dependencies outside the immediate scope:
  - Add a `code_paths` parameter to `mlflow.pytorch.save_model` and `mlflow.pytorch.log_model` to allow external module dependencies to be specified as paths to python files. (#842, @dbczumar)
  - Improve `mlflow.pytorch.save_model` to capture class definitions from notebooks and the `__main__` scope (#851, #861, @dbczumar)
- [Runs/R] Allow client to infer context info when creating new run in fluent API (#958, @tomasatdatabricks)
- [Runs/UI] Support Git Commit hyperlink for Gitlab and Bitbucket. Previously the clickable hyperlink was generated only for Github pages. (#901)
- [Search]/API] Allow param value to have any content, not just alphanumeric characters, `.`, and `-` (#788, @mparkhe)
- [Search/API] Support "filter" string in the `SearchRuns` API. Corresponding UI improvements are planned for the future (#905, @mparkhe)
- [Logging] Basic support for LogBatch. NOTE: The feature is currently experimental and the behavior is expected to change in the near future. (#950, #951, #955, #1001, @smurching)

Bug fixes and documentation updates:

- [Artifacts] Fix empty-file upload to DBFS in `log_artifact` and `log_artifacts` (#895, #818, @smurching)
- [Artifacts] S3 artifact store: fix path resolution error when artifact root is bucket root (#928, @dbczumar)
- [UI] Fix a bug with Databricks notebook URL links (#891, @smurching)
- [Export] Fix for missing run name in csv export (#864, @jimthompson5802)
- [Example] Correct missing tensorboardX module error in PyTorch example when running in MLflow Docker container (#809, @jimthompson5802)
- [Scoring/R] Fix local serving of rfunc models (#874, @kevinykuo)
- [Docs] Improve flavor-specific documentation in Models documentation (#909, @dbczumar)

Small bug fixes and doc updates (#822, #899, #787, #785, #780, #942, @hanyucui; #862, #904, #954, #806, #857, #845, @stbof; #907, #872, @smurching; #896, #858, #836, #859, #923, #939, #933, #931, #952, @dbczumar; #880, @zblz; #876, @acroz; #827, #812, #816, #829, @jimthompson5802; #837, #790, #897, #974, #900, @mparkhe; #831, #798, @aarondav; #814, @sueann; #824, #912, @mateiz; #922, #947, @tomasatdatabricks; #795, @KevYuen; #676, @mlaradji; #906, @4n4nd; #777, @tmielika; #804, @alkersan)

## 0.8.2 (2019-01-28)

MLflow 0.8.2 is a patch release on top of 0.8.1 containing only bug fixes and no breaking changes or features.

Bug fixes:

- [Python API] CloudPickle has been added to the set of MLflow library dependencies, fixing missing import errors when attempting to save models (#777, @tmielika)
- [Python API] Fixed a malformed logging call that prevented `mlflow.sagemaker.push_image_to_ecr()` invocations from succeeding (#784, @jackblandin)
- [Models] PyTorch models can now be saved with code dependencies, allowing model classes to be loaded successfully in new environments (#842, #836, @dbczumar)
- [Artifacts] Fixed a timeout when logging zero-length files to DBFS artifact stores (#818, @smurching)

Small docs updates (#845, @stbof; #840, @grahamhealy20; #839, @wilderrodrigues)

## 0.8.1 (2018-12-21)

MLflow 0.8.1 introduces several significant improvements:

- Improved UI responsiveness and load time, especially when displaying experiments containing hundreds to thousands of runs.
- Improved visualizations, including interactive scatter plots for MLflow run comparisons
- Expanded support for scoring Python models as Spark UDFs. For more information, see the [updated documentation for this feature](https://mlflow.org/docs/latest/models.html#export-a-python-function-model-as-an-apache-spark-udf).
- By default, saved models will now include a Conda environment specifying all of the dependencies necessary for loading them in a new environment.

Features:

- [API/CLI] Support for running MLflow projects from ZIP files (#759, @jmorefieldexpe)
- [Python API] Support for passing model conda environments as dictionaries to `save_model` and `log_model` functions (#748, @dbczumar)
- [Models] Default Anaconda environments have been added to many Python model flavors. By default, models produced by `save_model` and `log_model` functions will include an environment that specifies all of the versioned dependencies necessary to load and serve the models. Previously, users had to specify these environments manually. (#705, #707, #708, #749, @dbczumar)
- [Scoring] Support for synchronous deployment of models to SageMaker (#717, @dbczumar)
- [Tracking] Include the Git repository URL as a tag when tracking an MLflow run within a Git repository (#741, @whiletruelearn, @mateiz)
- [UI] Improved runs UI performance by using a react-virtualized table to optimize row rendering (#765, #762, #745, @smurching)
- [UI] Significant performance improvements for rendering run metrics, tags, and parameter information (#764, #747, @smurching)
- [UI] Scatter plots, including run comparison plots, are now interactive (#737, @mateiz)
- [UI] Extended CSRF support by allowing the MLflow UI server to specify a set of expected headers that clients should set when making AJAX requests (#733, @aarondav)

Bug fixes and documentation updates:

- [Python/Scoring] MLflow Python models that produce Pandas DataFrames can now be evaluated as Spark UDFs correctly. Spark UDF outputs containing multiple columns of primitive types are now supported (#719, @tomasatdatabricks)
- [Scoring] Fixed a serialization error that prevented models served with Azure ML from returning Pandas DataFrames (#754, @dbczumar)
- [Docs] New example demonstrating how the MLflow REST API can be used to create experiments and log run information (#750, kjahan)
- [Docs] R documentation has been updated for clarity and style consistency (#683, @stbof)
- [Docs] Added clarification about user setup requirements for executing remote MLflow runs on Databricks (#736, @andyk)

Small bug fixes and doc updates (#768, #715, @smurching; #728, dodysw; #730, mshr-h; #725, @kryptec; #769, #721, @dbczumar; #714, @stbof)

## 0.8.0 (2018-11-08)

MLflow 0.8.0 introduces several major features:

- Dramatically improved UI for comparing experiment run results:

  - Metrics and parameters are by default grouped into a single column, to avoid an explosion of mostly-empty columns. Individual metrics and parameters can be moved into their own column to help compare across rows.
  - Runs that are "nested" inside other runs (e.g., as part of a hyperparameter search or multistep workflow) now show up grouped by their parent run, and can be expanded or collapsed altogether. Runs can be nested by calling `mlflow.start_run` or `mlflow.run` while already within a run.
  - Run names (as opposed to automatically generated run UUIDs) now show up instead of the run ID, making comparing runs in graphs easier.
  - The state of the run results table, including filters, sorting, and expanded rows, is persisted in browser local storage, making it easier to go back and forth between an individual run view and the table.

- Support for deploying models as Docker containers directly to Azure Machine Learning Service Workspace (as opposed to the previously-recommended solution of Azure ML Workbench).

Breaking changes:

- [CLI] `mlflow sklearn serve` has been removed in favor of `mlflow pyfunc serve`, which takes the same arguments but works against any pyfunc model (#690, @dbczumar)

Features:

- [Scoring] pyfunc server and SageMaker now support the pandas "split" JSON format in addition to the "records" format. The split format allows the client to specify the order of columns, which is necessary for some model formats. We recommend switching client code over to use this new format (by sending the Content-Type header `application/json; format=pandas-split`), as it will become the default JSON format in MLflow 0.9.0. (#690, @dbczumar)
- [UI] Add compact experiment view (#546, #620, #662, #665, @smurching)
- [UI] Add support for viewing & tracking nested runs in experiment view (#588, @andrewmchen; #618, #619, @aarondav)
- [UI] Persist experiments view filters and sorting in browser local storage (#687, @smurching)
- [UI] Show run name instead of run ID when present (#476, @smurching)
- [Scoring] Support for deploying Models directly to Azure Machine Learning Service Workspace (#631, @dbczumar)
- [Server/Python/Java] Add `rename_experiment` to Tracking API (#570, @aarondav)
- [Server] Add `get_experiment_by_name` to RestStore (#592, @dmarkhas)
- [Server] Allow passing gunicorn options when starting mlflow server (#626, @mparkhe)
- [Python] Cloudpickle support for sklearn serialization (#653, @dbczumar)
- [Artifacts] FTP artifactory store added (#287, @Shenggan)

Bug fixes and documentation updates:

- [Python] Update TensorFlow integration to match API provided by other flavors (#612, @dbczumar; #670, @mlaradji)
- [Python] Support for TensorFlow 1.12 (#692, @smurching)
- [R] Explicitly loading Keras module at predict time no longer required (#586, @kevinykuo)
- [R] pyfunc serve can correctly load models saved with the R Keras support (#634, @tomasatdatabricks)
- [R] Increase network timeout of calls to the RestStore from 1 second to 60 seconds (#704, @aarondav)
- [Server] Improve errors returned by RestStore (#582, @andrewmchen; #560, @smurching)
- [Server] Deleting the default experiment no longer causes it to be immediately recreated (#604, @andrewmchen; #641, @schipiga)
- [Server] Azure Blob Storage artifact repo supports Windows paths (#642, @marcusrehm)
- [Server] Improve behavior when environment and run files are corrupted (#632, #654, #661, @mparkhe)
- [UI] Improve error page when viewing nonexistent runs or views (#600, @andrewmchen; #560, @andrewmchen)
- [UI] UI no longer throws an error if all experiments are deleted (#605, @andrewmchen)
- [Docs] Include diagram of workflow for multistep example (#581, @dennyglee)
- [Docs] Add reference tags and R and Java APIs to tracking documentation (#514, @stbof)
- [Docs/R] Use CRAN installation (#686, @javierluraschi)

Small bug fixes and doc updates (#576, #594, @javierluraschi; #585, @kevinykuo; #593, #601, #611, #650, #669, #671, #679, @dbczumar; #607, @suzil; #583, #615, @andrewmchen; #622, #681, @aarondav; #625, @pogil; #589, @tomasatdatabricks; #529, #635, #684, @stbof; #657, @mvsusp; #682, @mateiz; #678, vfdev-5; #596, @yutannihilation; #663, @smurching)

## 0.7.0 (2018-10-01)

MLflow 0.7.0 introduces several major features:

- An R client API (to be released on CRAN soon)
- Support for deleting runs (API + UI)
- UI support for adding notes to a run

The release also includes bugfixes and improvements across the Python and Java clients, tracking UI,
and documentation.

Breaking changes:

- [Python] The per-flavor implementation of load_pyfunc has been made private (#539, @tomasatdatabricks)
- [REST API, Java] logMetric now accepts a double metric value instead of a float (#566, @aarondav)

Features:

- [R] Support for R (#370, #471, @javierluraschi; #548 @kevinykuo)
- [UI] Add support for adding notes to Runs (#396, @aadamson)
- [Python] Python API, REST API, and UI support for deleting Runs (#418, #473, #526, #579 @andrewmchen)
- [Python] Set a tag containing the branch name when executing a branch of a Git project (#469, @adrian555)
- [Python] Add a set_experiment API to activate an experiment before starting runs (#462, @mparkhe)
- [Python] Add arguments for specifying a parent run to tracking & projects APIs (#547, @andrewmchen)
- [Java] Add Java set tag API (#495, @smurching)
- [Python] Support logging a conda environment with sklearn models (#489, @dbczumar)
- [Scoring] Support downloading MLflow scoring JAR from Maven during scoring container build (#507, @dbczumar)

Bug fixes:

- [Python] Print errors when the Databricks run fails to start (#412, @andrewmchen)
- [Python] Fix Spark ML PyFunc loader to work on Spark driver (#480, @tomasatdatabricks)
- [Python] Fix Spark ML load_pyfunc on distributed clusters (#490, @tomasatdatabricks)
- [Python] Fix error when downloading artifacts from a run's artifact root (#472, @dbczumar)
- [Python] Fix DBFS upload file-existence-checking logic during Databricks project execution (#510, @smurching)
- [Python] Support multi-line and unicode tags (#502, @mparkhe)
- [Python] Add missing DeleteExperiment, RestoreExperiment implementations in the Python REST API client (#551, @mparkhe)
- [Scoring] Convert Spark DataFrame schema to an MLeap schema prior to serialization (#540, @dbczumar)
- [UI] Fix bar chart always showing in metric view (#488, @smurching)

Small bug fixes and doc updates (#467 @drorata; #470, #497, #508, #518 @dbczumar; #455, #466, #492, #504, #527 @aarondav; #481, #475, #484, #496, #515, #517, #498, #521, #522, #573 @smurching; #477 @parkerzf; #494 @jainr; #501, #531, #532, #552 @mparkhe; #503, #520 @dmatrix; #509, #532 @tomasatdatabricks; #484, #486 @stbof; #533, #534 @javierluraschi; #542 @GCBallesteros; #511 @AdamBarnhard)

## 0.6.0 (2018-09-10)

MLflow 0.6.0 introduces several major features:

- A Java client API, available on Maven
- Support for saving and serving SparkML models as MLeap for low-latency serving
- Support for tagging runs with metadata, during and after the run completion
- Support for deleting (and restoring deleted) experiments

In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. The [examples](https://github.com/mlflow/mlflow/tree/master/examples) subdirectory has also been revamped to make it easier to jump in, and examples demonstrating multistep workflows and hyperparameter tuning have been added.

Breaking changes:

We fixed a few inconsistencies in the the `mlflow.tracking` API, as introduced in 0.5.0:

- `MLflowService` has been renamed `MlflowClient` (#461, @mparkhe)
- You get an `MlflowClient` by calling `mlflow.tracking.MlflowClient()` (previously, this was `mlflow.tracking.get_service()`) (#461, @mparkhe)
- `MlflowService.list_runs` was changed to `MlflowService.list_run_infos` to reflect the information actually returned by the call. It now returns a `RunInfo` instead of a `Run` (#334, @aarondav)
- `MlflowService.log_artifact` and `MlflowService.log_artifacts` now take a `run_id` instead of `artifact_uri`. This now matches `list_artifacts` and `download_artifacts` (#444, @aarondav)

Features:

- Java client API added with support for the MLflow Tracking API (analogous to `mlflow.tracking`), allowing users to create and manage experiments, runs, and artifacts. The release includes a [usage example](https://github.com/mlflow/mlflow/blob/master/mlflow/java/client/src/main/java/org/mlflow/tracking/samples/QuickStartDriver.java>)and [Javadocs](https://mlflow.org/docs/latest/java_api/index.html). The client is published to Maven under `mlflow:mlflow` (#380, #394, #398, #409, #410, #430, #452, @aarondav)
- SparkML models are now also saved in MLeap format (https://github.com/combust/mleap), when applicable. Model serving platforms can choose to serve using this format instead of the SparkML format to dramatically decrease prediction latency. SageMaker now does this by default (#324, #327, #331, #395, #428, #435, #438, @dbczumar)
- [API] Experiments can now be deleted and restored via REST API, Python Tracking API, and MLflow CLI (#340, #344, #367, @mparkhe)
- [API] Tags can now be set via a SetTag API, and they have been moved to `RunData` from `RunInfo` (#342, @aarondav)
- [API] Added `list_artifacts` and `download_artifacts` to `MlflowService` to interact with a run's artifactory (#350, @andrewmchen)
- [API] Added `get_experiment_by_name` to Python Tracking API, and equivalent to Java API (#373, @vfdev-5)
- [API/Python] Version is now exposed via `mlflow.__version__`.
- [API/CLI] Added `mlflow artifacts` CLI to list, download, and upload to run artifact repositories (#391, @aarondav)
- [UI] Added icons to source names in MLflow Experiments UI (#381, @andrewmchen)
- [UI] Added support to view `.log` and `.tsv` files from MLflow artifacts UI (#393, @Shenggan; #433, @whiletruelearn)
- [UI] Run names can now be edited from within the MLflow UI (#382, @smurching)
- [Serving] Added `--host` option to `mlflow serve` to allow listening on non-local addresses (#401, @hamroune)
- [Serving/SageMaker] SageMaker serving takes an AWS region argument (#366, @dbczumar)
- [Python] Added environment variables to support providing HTTP auth (username, password, token) when talking to a remote MLflow tracking server (#402, @aarondav)
- [Python] Added support to override S3 endpoint for S3 artifactory (#451, @hamroune)
- MLflow nightly Python wheel and JAR snapshots are now available and linked from https://github.com/mlflow/mlflow (#352, @aarondav)

Bug fixes and documentation updates:

- [Python] `mlflow run` now logs default parameters, in addition to explicitly provided ones (#392, @mparkhe)
- [Python] `log_artifact` in FileStore now requires a relative path as the artifact path (#439, @mparkhe)
- [Python] Fixed string representation of Python entities, so they now display both their type and serialized fields (#371, @smurching)
- [UI] Entry point name is now shown in MLflow UI (#345, @aarondav)
- [Models] Keras model export now includes TensorFlow graph explicitly to ensure the model can always be loaded at deployment time (#440, @tomasatdatabricks)
- [Python] Fixed issue where FileStore ignored provided Run Name (#358, @adrian555)
- [Python] Fixed an issue where any `mlflow run` failing printed an extraneous exception (#365, @smurching)
- [Python] uuid dependency removed (#351, @antonpaquin)
- [Python] Fixed issues with remote execution on Databricks (#357, #361, @smurching; #383, #387, @aarondav)
- [Docs] Added [comprehensive example](https://github.com/mlflow/mlflow/tree/master/examples/multistep_workflow) of doing a multistep workflow, chaining MLflow runs together and reusing results (#338, @aarondav)
- [Docs] Added [comprehensive example](https://github.com/mlflow/mlflow/tree/master/examples/hyperparam) of doing hyperparameter tuning (#368, @tomasatdatabricks)
- [Docs] Added code examples to `mlflow.keras` API (#341, @dmatrix)
- [Docs] Significant improvements to Python API documentation (#454, @stbof)
- [Docs] Examples folder refactored to improve readability. The examples now reside in `examples/` instead of `example/`, too (#399, @mparkhe)
- Small bug fixes and doc updates (#328, #363, @ToonKBC; #336, #411, @aarondav; #284, @smurching; #377, @mparkhe; #389, gioa; #408, @aadamson; #397, @vfdev-5; #420, @adrian555; #459, #463, @stbof)

## 0.5.2 (2018-08-24)

MLflow 0.5.2 is a patch release on top of 0.5.1 containing only bug fixes and no breaking changes or features.

Bug fixes:

- Fix a bug with ECR client creation that caused `mlflow.sagemaker.deploy()` to fail when searching for a deployment Docker image (#366, @dbczumar)

## 0.5.1 (2018-08-23)

MLflow 0.5.1 is a patch release on top of 0.5.0 containing only bug fixes and no breaking changes or features.

Bug fixes:

- Fix `with mlflow.start_run() as run` to actually set `run` to the created Run (previously, it was None) (#322, @tomasatdatabricks)
- Fixes to DBFS artifactory to throw an exception if logging an artifact fails (#309) and to mimic FileStore's behavior of logging subdirectories (#347, @andrewmchen)
- Fix for Python 3.7 support with tarfiles (#329, @tomasatdatabricks)
- Fix spark.load_model not to delete the DFS tempdir (#335, @aarondav)
- MLflow UI now appropriately shows entrypoint if it's not main (#345, @aarondav)
- Make Python API forward-compatible with newer server versions of protos (#348, @aarondav)
- Improved API docs (#305, #284, @smurching)

## 0.5.0 (2018-08-17)

MLflow 0.5.0 offers some major improvements, including Keras and PyTorch first-class support as models, SFTP support as an artifactory, a new scatterplot visualization to compare runs, and a more complete Python SDK for experiment and run management.

Breaking changes:

- The Tracking API has been split into two pieces, a "basic logging" API and a "tracking service" API. The "basic logging" API deals with logging metrics, parameters, and artifacts to the currently-active active run, and is accessible in `mlflow` (e.g., `mlflow.log_param`). The tracking service API allow managing experiments and runs (especially historical runs) and is available in `mlflow.tracking`. The tracking service API will look analogous to the upcoming R and Java Tracking Service SDKs. Please be aware of the following breaking changes:

  - `mlflow.tracking` no longer exposes the basic logging API, only `mlflow`. So, code that was written like `from mlflow.tracking import log_param` will have to be `from mlflow import log_param` (note that almost all examples were already doing this).
  - Access to the service API goes through the `mlflow.tracking.get_service()` function, which relies on the same tracking server set by either the environment variable `MLFLOW_TRACKING_URI` or by code with `mlflow.tracking.set_tracking_uri()`. So code that used to look like `mlflow.tracking.get_run()` will now have to do `mlflow.tracking.get_service().get_run()`. This does not apply to the basic logging API.
  - `mlflow.ActiveRun` has been converted into a lightweight wrapper around `mlflow.entities.Run` to enable the Python `with` syntax. This means that there are no longer any special methods on the object returned when calling `mlflow.start_run()`. These can be converted to the service API.

  - The Python entities returned by the tracking service API are now accessible in `mlflow.entities` directly. Where previously you may have used `mlflow.entities.experiment.Experiment`, you would now just use `mlflow.entities.Experiment`. The previous version still exists, but is deprecated and may be hidden in a future version.

- REST API endpoint `/ajax-api/2.0/preview/mlflow/artifacts/get` has been moved to `$static_prefix/get-artifact`. This change is coversioned in the JavaScript, so should not be noticeable unless you were calling the REST API directly (#293, @andremchen)

Features:

- [Models] Keras integration: we now support logging Keras models directly in the log_model API, model format, and serving APIs (#280, @ToonKBC)
- [Models] PyTorch integration: we now support logging PyTorch models directly in the log_model API, model format, and serving APIs (#264, @vfdev-5)
- [UI] Scatterplot added to "Compare Runs" view to help compare runs using any two metrics as the axes (#268, @ToonKBC)
- [Artifacts] SFTP artifactory store added (#260, @ToonKBC)
- [Sagemaker] Users can specify a custom VPC when deploying SageMaker models (#304, @dbczumar)
- Pyfunc serialization now includes the Python version, and warns if the major version differs (can be suppressed by using `load_pyfunc(suppress_warnings=True)`) (#230, @dbczumar)
- Pyfunc serve/predict will activate conda environment stored in MLModel. This can be disabled by adding `--no-conda` to `mlflow pyfunc serve` or `mlflow pyfunc predict` (#225, @0wu)
- Python SDK formalized in `mlflow.tracking`. This includes adding SDK methods for `get_run`, `list_experiments`, `get_experiment`, and `set_terminated`. (#299, @aarondav)
- `mlflow run` can now be run against projects with no `conda.yaml` specified. By default, an empty conda environment will be created -- previously, it would just fail. You can still pass `--no-conda` to avoid entering a conda environment altogether (#218, @smurching)

Bug fixes:

- Fix numpy array serialization for int64 and other related types, allowing pyfunc to return such results (#240, @arinto)
- Fix DBFS artifactory calling `log_artifacts` with binary data (#295, @aarondav)
- Fix Run Command shown in UI to reproduce a run when the original run is targeted at a subdirectory of a Git repo (#294, @adrian555)
- Filter out ubiquitous dtype/ufunc warning messages (#317, @aarondav)
- Minor bug fixes and documentation updates (#261, @stbof; #279, @dmatrix; #313, @rbang1, #320, @yassineAlouini; #321, @tomasatdatabricks; #266, #282, #289, @smurching; #267, #265, @aarondav; #256, #290, @ToonKBC; #273, #263, @mateiz; #272, #319, @adrian555; #277, @aadamson; #283, #296, @andrewmchen)

## 0.4.2 (2018-08-07)

Breaking changes: None

Features:

- MLflow experiments REST API and `mlflow experiments create` now support providing `--artifact-location` (#232, @aarondav)
- [UI] Runs can now be sorted by columns, and added a Select All button (#227, @ToonKBC)
- Databricks File System (DBFS) artifactory support added (#226, @andrewmchen)
- databricks-cli version upgraded to >= 0.8.0 to support new DatabricksConfigProvider interface (#257, @aarondav)

Bug fixes:

- MLflow client sends REST API calls using snake_case instead of camelCase field names (#232, @aarondav)
- Minor bug fixes (#243, #242, @aarondav; #251, @javierluraschi; #245, @smurching; #252, @mateiz)

## 0.4.1 (2018-08-03)

Breaking changes: None

Features:

- [Projects] MLflow will use the conda installation directory given by the `$MLFLOW_CONDA_HOME`
  if specified (e.g. running conda commands by invoking `$MLFLOW_CONDA_HOME/bin/conda`), defaulting
  to running "conda" otherwise. (#231, @smurching)
- [UI] Show GitHub links in the UI for projects run from http(s):// GitHub URLs (#235, @smurching)

Bug fixes:

- Fix GCSArtifactRepository issue when calling list_artifacts on a path containing nested directories (#233, @jakeret)
- Fix Spark model support when saving/loading models to/from distributed filesystems (#180, @tomasatdatabricks)
- Add missing mlflow.version import to sagemaker module (#229, @dbczumar)
- Validate metric, parameter and run IDs in file store and Python client (#224, @mateiz)
- Validate that the tracking URI is a remote URI for Databricks project runs (#234, @smurching)
- Fix bug where we'd fetch git projects at SSH URIs into a local directory with the same name as
  the URI, instead of into a temporary directory (#236, @smurching)

## 0.4.0 (2018-08-01)

Breaking changes:

- [Projects] Removed the `use_temp_cwd` argument to `mlflow.projects.run()`
  (`--new-dir` flag in the `mlflow run` CLI). Runs of local projects now use the local project
  directory as their working directory. Git projects are still fetched into temporary directories
  (#215, @smurching)
- [Tracking] GCS artifact storage is now a pluggable dependency (no longer installed by default).
  To enable GCS support, install `google-cloud-storage` on both the client and tracking server via pip.
  (#202, @smurching)
- [Tracking] Clients running MLflow 0.4.0 and above require a server running MLflow 0.4.0
  or above, due to a fix that ensures clients no longer double-serialize JSON into strings when
  sending data to the server (#200, @aarondav). However, the MLflow 0.4.0 server remains
  backwards-compatible with older clients (#216, @aarondav)

Features:

- [Examples] Add a more advanced tracking example: using MLflow with PyTorch and TensorBoard (#203)
- [Models] H2O model support (#170, @ToonKBC)
- [Projects] Support for running projects in subdirectories of Git repos (#153, @juntai-zheng)
- [SageMaker] Support for specifying a compute specification when deploying to SageMaker (#185, @dbczumar)
- [Server] Added --static-prefix option to serve UI from a specified prefix to MLflow UI and server (#116, @andrewmchen)
- [Tracking] Azure blob storage support for artifacts (#206, @mateiz)
- [Tracking] Add support for Databricks-backed RestStore (#200, @aarondav)
- [UI] Enable productionizing frontend by adding CSRF support (#199, @aarondav)
- [UI] Update metric and parameter filters to let users control column order (#186, @mateiz)

Bug fixes:

- Fixed incompatible file structure returned by GCSArtifactRepository (#173, @jakeret)
- Fixed metric values going out of order on x axis (#204, @mateiz)
- Fixed occasional hanging behavior when using the projects.run API (#193, @smurching)

- Miscellaneous bug and documentation fixes from @aarondav, @andrewmchen, @arinto, @jakeret, @mateiz, @smurching, @stbof

## 0.3.0 (2018-07-18)

Breaking changes:

- [MLflow Server] Renamed `--artifact-root` parameter to `--default-artifact-root` in `mlflow server` to better reflect its purpose (#165, @aarondav)

Features:

- Spark MLlib integration: we now support logging SparkML Models directly in the log_model API, model format, and serving APIs (#72, @tomasatdatabricks)
- Google Cloud Storage is now supported as an artifact storage root (#152, @bnekolny)
- Support asychronous/parallel execution of MLflow runs (#82, @smurching)
- [SageMaker] Support for deleting, updating applications deployed via SageMaker (#145, @dbczumar)
- [SageMaker] Pushing the MLflow SageMaker container now includes the MLflow version that it was published with (#124, @sueann)
- [SageMaker] Simplify parameters to SageMaker deploy by providing sane defaults (#126, @sueann)
- [UI] One-element metrics are now displayed as a bar char (#118, @cryptexis)

Bug fixes:

- Require gitpython>=2.1.0 (#98, @aarondav)
- Fixed TensorFlow model loading so that columns match the output names of the exported model (#94, @smurching)
- Fix SparkUDF when number of columns >= 10 (#97, @aarondav)
- Miscellaneous bug and documentation fixes from @emres, @dmatrix, @stbof, @gsganden, @dennyglee, @anabranch, @mikehuston, @andrewmchen, @juntai-zheng

## 0.2.1 (2018-06-28)

This is a patch release fixing some smaller issues after the 0.2.0 release.

- Switch protobuf implementation to C, fixing a bug related to tensorflow/mlflow import ordering (issues #33 and #77, PR #74, @andrewmchen)
- Enable running mlflow server without git binary installed (#90, @aarondav)
- Fix Spark UDF support when running on multi-node clusters (#92, @aarondav)

## 0.2.0 (2018-06-27)

- Added `mlflow server` to provide a remote tracking server. This is akin to `mlflow ui` with new options:

  - `--host` to allow binding to any ports (#27, @mdagost)
  - `--artifact-root` to allow storing artifacts at a remote location, S3 only right now (#78, @mateiz)
  - Server now runs behind gunicorn to allow concurrent requests to be made (#61, @mateiz)

- TensorFlow integration: we now support logging TensorFlow Models directly in the log_model API, model format, and serving APIs (#28, @juntai-zheng)
- Added `experiments.list_experiments` as part of experiments API (#37, @mparkhe)
- Improved support for unicode strings (#79, @smurching)
- Diabetes progression example dataset and training code (#56, @dennyglee)
- Miscellaneous bug and documentation fixes from @Jeffwan, @yupbank, @ndjido, @xueyumusic, @manugarri, @tomasatdatabricks, @stbof, @andyk, @andrewmchen, @jakeret, @0wu, @aarondav

## 0.1.0 (2018-06-05)

- Initial version of mlflow.