mlops.md

Machine Learning Operations (MLOps) Standards

This document defines Bayat's standards for machine learning operations (MLOps), covering the end-to-end lifecycle of machine learning models from development to deployment and monitoring.

Table of Contents

• Introduction
• ML Project Structure
• Experiment Tracking
• Data Management
• Model Development
• Model Evaluation
• Model Versioning
• Model Registry
• Deployment Patterns
• Monitoring and Observability
• Feedback Loops
• Ethics and Responsible AI
• Implementation Checklist

Introduction

Machine Learning Operations (MLOps) is a set of practices that combines Machine Learning, DevOps, and Data Engineering to deploy and maintain ML models in production reliably and efficiently.

Goals of MLOps

1. Reproducibility: Ensure ML experiments and models can be reproduced
2. Scalability: Enable efficient scaling of ML workflows
3. Collaboration: Facilitate teamwork between data scientists, ML engineers, and operations
4. Monitoring: Track model performance and data quality in production
5. Governance: Enforce standards and security policies for ML systems
6. Automation: Reduce manual steps in the ML lifecycle
7. Traceability: Maintain clear lineage from data to deployed models

ML Project Structure

All ML projects should follow a standardized structure:

ml-project/
├── README.md               # Project overview, setup instructions
├── LICENSE                 # License information
├── .gitignore              # Git ignore file
├── .env.example            # Example environment variables
├── pyproject.toml          # Project metadata and dependencies
├── Makefile                # Common tasks automation
├── Dockerfile              # Container definition
├── data/                   # Data directory (often gitignored)
│   ├── raw/                # Raw data (immutable)
│   ├── processed/          # Processed data
│   ├── features/           # Feature stores
│   └── external/           # External data sources
├── notebooks/              # Jupyter notebooks for exploration
│   ├── exploratory/        # Early exploration
│   └── reports/            # Report notebooks
├── src/                    # Source code
│   ├── __init__.py
│   ├── data/               # Data processing
│   │   ├── __init__.py
│   │   ├── ingest.py       # Data ingestion
│   │   ├── preprocess.py   # Data preprocessing
│   │   ├── validation.py   # Data validation
│   │   └── features.py     # Feature engineering
│   ├── models/             # Model code
│   │   ├── __init__.py
│   │   ├── train.py        # Training scripts
│   │   ├── evaluate.py     # Evaluation scripts
│   │   └── predict.py      # Prediction scripts
│   ├── utils/              # Utility functions
│   │   ├── __init__.py
│   │   └── logging.py      # Logging utilities
│   └── visualization/      # Visualization code
│       ├── __init__.py
│       └── visualize.py    # Visualization functions
├── configs/                # Configuration files
│   ├── model_config.yaml   # Model parameters
│   └── pipeline_config.yaml # Pipeline parameters
├── tests/                  # Test code
│   ├── __init__.py
│   ├── test_data.py        # Tests for data processing
│   └── test_models.py      # Tests for models
├── artifacts/              # Generated artifacts (gitignored)
│   ├── models/             # Saved models
│   └── visualizations/     # Generated plots
├── docs/                   # Documentation
│   ├── model_card.md       # Model card documentation
│   └── data_dictionary.md  # Data dictionary
└── pipelines/              # CI/CD pipeline definitions
    ├── training_pipeline.py # Training pipeline
    └── inference_pipeline.py # Inference pipeline

Experiment Tracking

Experiment Management

1. Required Tools: Use an experiment tracking tool (e.g., MLflow, Weights & Biases, or DVC)
2. Experiment Metadata: Each experiment must track:
   • Hyperparameters
   • Dataset versions
   • Code version
   • Environment specifications
   • Performance metrics
   • Artifacts (models, visualizations)
3. Experiment Naming: Use consistent naming convention {project_name}-{experiment_type}-{date}

Reproducibility Requirements

1. Environment Management: Use virtual environments and dependency pinning
2. Seed Values: Set random seeds for all stochastic processes
3. Code Versioning: Tag or commit the exact code version used for each experiment
4. Data Versioning: Version datasets using DVC or similar tools

Data Management

Data Versioning

1. Immutable Raw Data: Never modify raw data directly
2. Versioned Transformations: Version all data transformations
3. Data Registry: Maintain a central registry of datasets with metadata
4. Data Lineage: Track data provenance and transformations

Data Quality

1. Validation Schemas: Define expectations for data using Great Expectations or similar tools
2. Automated Testing: Run automated data quality tests
3. Data Drift Detection: Implement mechanisms to detect data drift
4. Data Documentation: Maintain data dictionaries and catalog

Model Development

Framework Standards

1. Approved Frameworks: Use PyTorch, TensorFlow, or scikit-learn for model development
2. Framework Versions: Specify and pin framework versions
3. Custom Models: Custom models must follow object-oriented design patterns

Training Best Practices

1. Modular Code: Separate model definition from training logic
2. Configuration: Use configuration files for hyperparameters
3. Resource Management: Implement checkpointing and early stopping
4. Distributed Training: For large models, use distributed training frameworks

Model Evaluation

Evaluation Metrics

1. Standard Metrics: Implement appropriate metrics for the model type
2. Baseline Comparison: Always compare against baseline models
3. Cross-Validation: Use cross-validation for reliable performance estimation
4. Performance Tracking: Track metrics across model versions

Evaluation Protocols

1. Holdout Sets: Maintain separate validation and test sets
2. Scenario Testing: Evaluate models under various scenarios
3. A/B Testing: Define protocols for A/B testing in production
4. Fairness Assessment: Evaluate model bias and fairness

Model Versioning

Versioning Scheme

1. Semantic Versioning: Use semantic versioning for models
2. Model Registry: Store models in a central model registry
3. Metadata: Include metadata such as:
   • Training date
   • Dataset version
   • Performance metrics
   • Creator
   • Intended use

Model Documentation

1. Model Cards: Create model cards for each model version
2. Usage Instructions: Document model inputs, outputs, and limitations
3. Performance Reports: Include benchmark results and performance analysis

Model Registry

Registry Requirements

1. Central Registry: Use a central model registry (e.g., MLflow Model Registry)
2. Access Control: Implement access control for model artifacts
3. Staging Levels: Define staging levels (development, staging, production)
4. Approval Process: Require approval for production deployment

Artifact Management

1. Storage Standards: Store models in a standardized format
2. Artifact Integrity: Implement checksums for artifact verification
3. Retention Policy: Define retention policies for model artifacts

Deployment Patterns

Deployment Methods

1. Containerization: Package models in containers
2. Infrastructure as Code: Define model deployments as code
3. CI/CD Integration: Integrate model deployment into CI/CD pipelines
4. Serving Options:
   • Online (real-time) inference
   • Batch inference
   • Edge deployment

Deployment Strategies

1. Blue-Green Deployment: Implement blue-green deployment for models
2. Canary Releases: Use canary releases for gradual rollout
3. Shadow Mode: Run new models in shadow mode before full deployment
4. Rollback Plan: Define clear rollback procedures

Monitoring and Observability

Monitoring Requirements

1. Monitoring Architecture: Implement a monitoring architecture for models
2. Key Metrics:
   • Prediction distribution
   • Feature drift
   • Model performance
   • System performance
   • Data quality
   • Business metrics

Alerting

1. Alert Thresholds: Define thresholds for monitoring metrics
2. Alert Channels: Set up appropriate alert channels
3. Incident Response: Define incident response procedures

Feedback Loops

Feedback Collection

1. Data Collection: Implement mechanisms to collect feedback data
2. Ground Truth Capture: Capture ground truth for continuous evaluation
3. User Feedback: Incorporate explicit user feedback where applicable

Continuous Improvement

1. Retraining Triggers: Define triggers for model retraining
2. Automatic Evaluation: Automatically evaluate new model versions
3. Performance Degradation: Define procedures for handling performance degradation

Ethics and Responsible AI

Ethical Standards

1. Bias Assessment: Assess models for potential bias
2. Fairness Metrics: Define and track fairness metrics
3. Transparency: Ensure model decisions can be explained
4. Privacy: Protect privacy in model training and inference

Governance

1. Approval Process: Implement an approval process for high-risk models
2. Documentation: Maintain comprehensive documentation of ethical considerations
3. Regular Audits: Conduct regular audits of model behavior

Implementation Checklist

• Set up standardized ML project structure
• Implement experiment tracking
• Establish data versioning and quality checks
• Define model development and evaluation standards
• Set up model registry and versioning
• Implement deployment pipelines
• Configure monitoring and alerting
• Establish feedback loops
• Document ethical considerations
• Train team on MLOps practices

Resources

• MLOps: Continuous delivery and automation pipelines in machine learning
• MLOps Maturity Model
• MLOps SIG