Merge branch 'main' into feat/hyperpod-tf

Author: kukushking

CHANGELOG.md

@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### **Changed**
 - updated `sagemaker-model-monitor` module and tested 4 types of monitoring end-to-end
+- added baseline generation step function to `sagemaker-model-monitor` module
 - updated lambda runtime and depdencies in `sagemaker-templates` module
 
 ## v2.0.0

modules/sagemaker/sagemaker-model-cicd/package-lock.json

@@ -13,13 +13,52 @@ Available monitoring types:
 * Model Bias
 * Model Explainability
 
-Note that updating parameters will require replacing resources. Deployments may be delayed until any
-running monitoring jobs complete (and the resources can be destroyed).
+The module includes an optional automated baseline generation feature that creates baseline statistics and constraints for your monitoring jobs.
 
 ### Architecture
 
 ![SageMaker Model Monitoring Module Architecture](docs/_static/sagemaker-model-monitoring-module-architecture.png "SageMaker Model Monitoring Module Architecture")
 
+1. **SageMaker Endpoint** - The deployed model endpoint being monitored
+    - **WARNING**: Data capture must be enabled for monitoring to function
+    - Captures inference requests and responses to S3
+
+2. **Baseline Generation (Optional)** - Automated baseline creation workflow
+    - **EventBridge Rule**: Triggers baseline generation on a schedule (default: daily at 2 AM UTC)
+    - **Step Functions State Machine**: Orchestrates the baseline generation process
+    - **Lambda Function (Docker)**: Invokes SageMaker Processing jobs for baseline calculation
+    - **SageMaker Processing Jobs**: Analyze training data to generate baseline statistics and constraints
+    - **S3 Baseline Output**: Stores generated baseline artifacts (constraints.json, statistics.json)
+
+3. **Ground Truth Data (Optional)** - Required for model quality and bias monitoring
+    - Actual "correct" labels created manually or by a workflow
+    - Example: actual customer churn for churn prediction models
+    - Stored in S3 and merged with captured inference data
+
+4. **Monitoring Jobs and Schedules** - Continuous monitoring execution
+    - **Monitoring Schedules**: Cron-based execution (minimum 1 hour interval)
+    - **SageMaker Monitoring Jobs**: Compare captured data against baselines
+    - Supports data quality, model quality, model bias, and model explainability monitoring
+
+5. **Monitoring Outputs** - Results and alerts
+    - **Violations Report**: Detailed violations file emitted to S3
+    - **CloudWatch Metrics**: Some monitoring types emit metrics (e.g., data quality drift)
+    - **CloudWatch Alarms**: Can be configured based on emitted metrics for automated alerting
+
+### Baseline Generation
+
+The module includes an optional automated baseline generation feature that creates baseline statistics and constraints for your monitoring jobs. 
+
+![SageMaker Model Monitoring Baseline Generation](docs/_static/sagemaker-model-monitoring-baseline.png "SageMaker Model Monitoring Baseline Generation")
+
+When you provide training data, the module will:
+
+1. Deploy a Step Functions state machine that orchestrates baseline generation
+2. Run SageMaker Processing jobs to analyze your training data
+3. Generate baseline statistics and constraints files
+4. Store the baseline artifacts in your specified S3 location
+5. Schedule automatic baseline regeneration (default: daily at 2 AM UTC)
+
 ## Inputs/Outputs
 
 ### Input Parameters
@@ -45,6 +84,19 @@ One or more of:
 - `sagemaker_project_name`: SageMaker project name
 - `tags`: Dictionary of tags to apply to resources
 
+#### Baseline Generation Parameters (Optional)
+
+These parameters control the automated baseline generation feature:
+
+- `baseline_training_data_s3_uri`: S3 URI for the training data used to generate baselines (e.g., `s3://bucket/path/to/training-data.csv`)
+- `baseline_output_data_s3_uri`: S3 URI where baseline statistics and constraints will be stored (e.g., `s3://bucket/path/to/baseline-output/`)
+- `baseline_instance_count`: Number of ML compute instances for baseline generation (default: 1)
+- `baseline_instance_type`: ML compute instance type for baseline generation (default: "ml.m5.xlarge")
+- `baseline_volume_size_gb`: Size of ML storage volume in GB for baseline generation (default: 20)
+- `baseline_max_runtime_seconds`: Maximum runtime in seconds for baseline generation jobs (default: 3600)
+
+**Note**: If `baseline_training_data_s3_uri` and `baseline_output_data_s3_uri` are provided, the module will automatically create a Step Functions state machine to generate and update baselines for all enabled monitoring types.
+
 ### Per-job Parameters
 
 #### Data Quality Monitoring Job Parameters
@@ -102,6 +154,8 @@ One or more of:
 
 ### Sample manifest declaration
 
+#### Basic Monitoring (without baseline generation)
+
 ```yaml
 name: monitoring
 path: modules/sagemaker/sagemaker-model-monitoring
@@ -116,11 +170,150 @@ parameters:
     value: DEMO-xgb-churn-pred-model-monitor-2025-10-23-19-11-16
   - name: enable_data_quality_monitor
     value: true
-  - name: data-quality-schedule-expression
+  - name: data_quality_schedule_expression
     value: cron(0 * ? * * *)
   - name: data_quality_baseline_s3_uri
     value: s3://sagemaker-us-east-2-<REDACTED>/SF-DEMO-xgb-churn-pred-model-monitor/baseline
   - name: data_quality_output_s3_uri
     value: s3://sagemaker-us-east-2-<REDACTED>/SF-DEMO-xgb-churn-pred-model-monitor/output
+```
+
+#### With Automated Baseline Generation
+
+```yaml
+name: monitoring
+path: modules/sagemaker/sagemaker-model-monitoring
+parameters:
+  - name: sagemaker_project_id
+    value: SF-DEMO-xgb-churn-pred-model-monitor-2025-10-23-19-11-16
+  - name: sagemaker_project_name
+    value: SF-DEMO-xgb-churn-pred-model-monitor-2025-10-23-19-11-16
+  - name: model_bucket_arn
+    value: arn:aws:s3:::sagemaker-us-east-2-<REDACTED>
+  - name: endpoint_name
+    value: DEMO-xgb-churn-pred-model-monitor-2025-10-23-19-11-16
+  # Baseline generation parameters
+  - name: baseline_training_data_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/training-data/train.csv
+  - name: baseline_output_data_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/baselines/
+  - name: baseline_instance_type
+    value: ml.m5.xlarge
+  - name: baseline_max_runtime_seconds
+    value: 3600
+  # Enable monitoring types
+  - name: enable_data_quality_monitor
+    value: true
+  - name: enable_model_quality_monitor
+    value: true
+  # Data quality monitoring
+  - name: data_quality_schedule_expression
+    value: cron(0 * ? * * *)
+  - name: data_quality_baseline_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/baselines/data-quality/
+  - name: data_quality_output_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/monitoring-output/data-quality/
+  # Model quality monitoring
+  - name: model_quality_schedule_expression
+    value: cron(0 * ? * * *)
+  - name: model_quality_baseline_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/baselines/model-quality/
+  - name: model_quality_output_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/monitoring-output/model-quality/
+  - name: model_quality_ground_truth_s3_uri
+    value: s3://sagemaker-us-east-2-<REDACTED>/ground-truth/
+  - name: model_quality_problem_type
+    value: BinaryClassification
+```
+
+### Sample Baseline Generation Events
+
+#### Step Functions Event Examples
+
+The baseline generation Step Functions state machine accepts events with the following structure. You can manually trigger the state machine with these event payloads:
+
+**Data Quality Baseline:**
+```json
+{
+  "monitor_type": "data_quality",
+  "endpoint_name": "my-endpoint",
+  "training_data_uri": "s3://my-bucket/training-data/train.csv",
+  "baseline_output_uri": "s3://my-bucket/baselines/data-quality/",
+  "data_quality_params": {
+    "instance_count": 1,
+    "instance_type": "ml.m5.xlarge",
+    "volume_size_gb": 30,
+    "max_runtime_seconds": 3600
+  }
+}
+```
+
+**Model Quality Baseline:**
+```json
+{
+  "monitor_type": "model_quality",
+  "endpoint_name": "my-endpoint",
+  "training_data_uri": "s3://my-bucket/training-data/train.csv",
+  "baseline_output_uri": "s3://my-bucket/baselines/model-quality/",
+  "model_quality_params": {
+    "instance_count": 1,
+    "instance_type": "ml.m5.xlarge",
+    "volume_size_gb": 30,
+    "max_runtime_seconds": 1800,
+    "problem_type": "BinaryClassification",
+    "inference_attribute": "prediction",
+    "probability_attribute": "probability",
+    "ground_truth_attribute": "label"
+  }
+}
+```
 
+**Model Bias Baseline:**
+```json
+{
+  "monitor_type": "model_bias",
+  "endpoint_name": "my-endpoint",
+  "training_data_uri": "s3://my-bucket/training-data/train.csv",
+  "baseline_output_uri": "s3://my-bucket/baselines/model-bias/",
+  "model_bias_params": {
+    "instance_count": 1,
+    "instance_type": "ml.m5.xlarge",
+    "volume_size_gb": 30,
+    "max_runtime_seconds": 1800,
+    "label_header": "label",
+    "headers": "feature1,feature2,feature3,label",
+    "dataset_type": "text/csv",
+    "label_values": "0,1",
+    "facet_name": "feature1",
+    "facet_values": "100,200",
+    "probability_threshold": 0.8,
+    "model_name": "my-model"
+  }
+}
 ```
+
+**Model Explainability Baseline:**
+```json
+{
+  "monitor_type": "model_explainability",
+  "endpoint_name": "my-endpoint",
+  "training_data_uri": "s3://my-bucket/training-data/train.csv",
+  "baseline_output_uri": "s3://my-bucket/baselines/model-explainability/",
+  "model_explainability_params": {
+    "instance_count": 1,
+    "instance_type": "ml.m5.xlarge",
+    "volume_size_gb": 30,
+    "max_runtime_seconds": 1800,
+    "label_header": "label",
+    "headers": "feature1,feature2,feature3,label",
+    "dataset_type": "text/csv",
+    "model_name": "my-model",
+    "num_samples": 100,
+    "agg_method": "mean_abs",
+    "save_local_shap_values": false,
+    "shap_baseline": [[0.0]]
+  }
+}
+```
+
+**Note**: All parameter fields within the `*_params` objects are optional and will use the defaults shown above if not provided.