SageMaker MLOps

Build production-grade ML workflows with SageMaker Pipelines, Model Registry, Model Monitor, and Feature Store for automated, reliable ML operations.

SageMaker Pipelines

SageMaker Pipelines is a purpose-built CI/CD service for machine learning. It allows you to define, automate, and manage end-to-end ML workflows:

• DAG definition: Define pipeline steps as a directed acyclic graph (DAG) using the SageMaker SDK
• Step types: Processing, Training, Transform, Register Model, Condition, Callback, and Lambda steps
• Parameterization: Pipeline parameters allow reuse with different inputs without code changes
• Caching: Step caching avoids re-running unchanged steps, saving time and cost
• Visualization: View pipeline DAGs, execution history, and step status in SageMaker Studio
• Scheduling: Trigger pipelines on schedule or in response to events via EventBridge

Model Registry

The Model Registry provides centralized model versioning, approval workflows, and metadata management:

• Model groups: Organize related model versions into logical groups
• Versioning: Track model versions with metadata, metrics, and lineage information
• Approval workflow: Models can be "Pending," "Approved," or "Rejected" - only approved models can be deployed
• Metadata: Store custom metadata, training metrics, and data lineage with each model version
• Deployment: Deploy directly from the registry with one click or programmatically

Model Monitor

Model Monitor continuously monitors deployed models for quality degradation:

Feature Store

SageMaker Feature Store provides a centralized repository for storing, sharing, and managing ML features:

• Online store: Low-latency feature retrieval for real-time inference (single-digit milliseconds)
• Offline store: Historical feature data in S3 for batch training and analysis
• Feature groups: Organize features into logical groups with schema definitions
• Time-travel queries: Query feature values at any point in time for reproducible training
• Cross-team sharing: Share features across teams and models to reduce duplication
• Automatic sync: Online and offline stores stay synchronized automatically

CI/CD for ML

Integrate SageMaker with AWS CI/CD services for automated ML workflows:

• CodeCommit/GitHub: Store training code, pipeline definitions, and configuration
• CodeBuild: Run tests, lint code, and build custom Docker containers
• CodePipeline: Orchestrate the full CI/CD flow from code change to production deployment
• EventBridge: Trigger pipelines based on events (S3 upload, schedule, model approval)
• CloudFormation/CDK: Define SageMaker infrastructure as code for reproducible environments