MLOPS02-BP04 Establish a lineage tracker system - Machine Learning Lens
Implementation guidanceResources

MLOPS02-BP04 Establish a lineage tracker system

Maintain a system that tracks changes for each release to enable reproducibility, speed up problem diagnosis, and improve regulatory adherence. Tracking lineage for model development includes changes in documentation, environment, model, data, code, and infrastructure.

Desired outcome: You have a comprehensive lineage tracking system that records the history of artifacts involved in your ML model development and deployment lifecycle. This system enables you to reproduce previous model versions, diagnose issues quickly, roll back to stable versions when needed, and improve your regulatory adherence. Your organization can trace model results back to their originating data sources, code versions, and infrastructure configurations.

Common anti-patterns:

  • Manually tracking changes in spreadsheets or documents.

  • Not capturing all dependent artifacts necessary for model reproduction.

  • Inconsistent tracking practices across teams.

  • Lacking integration between different components of the ML pipeline.

  • Failing to track infrastructure and environment configurations.

Benefits of establishing this best practice:

  • Enables reproducibility of model versions for debugging.

  • Accelerates problem diagnosis and resolution when issues arise.

  • Supports regulatory adherence and audit requirements.

  • Facilitates rollback to previous stable versions when needed.

  • Improves collaboration among team members with transparent tracking.

  • Enhances model governance and responsible AI practices.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Implementing a comprehensive lineage tracker system is essential for maintaining the traceability and reproducibility of your machine learning models. Without proper lineage tracking, your organization may struggle to debug issues, comply with regulations, or reproduce previous model versions when needed. The lineage tracker should capture information about components that influence your model's behavior, including the data used for training, preprocessing steps, hyperparameters, model architecture, evaluation metrics, and deployment environment.

A robust lineage tracking system starts with identifying the key artifacts that need to be tracked throughout the ML lifecycle. Once identified, you can use AWS services like SageMaker AI ML Lineage Tracking to automatically record and store the relationships between these artifacts. This information becomes invaluable when you need to audit your models, reproduce results, or diagnose issues in production.

For example, if a model suddenly begins producing unexpected results in production, your lineage tracking system should allow you to trace back to the exact version of the model, the data it was trained on, the code used to train it, and the infrastructure configuration at the time of deployment. This comprehensive tracking enables faster problem resolution and maintains trust in your ML systems.

Implementation steps

  1. Identify artifacts needed for tracking. Begin by identifying artifacts that contribute to your model's development and deployment. This includes raw data, processed data, feature sets, model parameters, code versions, training environments, and deployment configurations. Understanding what needs to be tracked is essential for meeting regulatory requirements and enabling reproducibility. Refer to Data and artifacts lineage tracking for guidance on the artifacts to include.

  2. Implement SageMaker AI ML Lineage Tracking. Use Amazon SageMaker AI ML Lineage Tracking to automatically record information about the steps in your ML workflow. SageMaker AI tracks relationships between datasets, algorithms, training jobs, and model artifacts, enabling you to reproduce workflows, track model and dataset lineage, and establish governance standards. The service creates entities for your ML workflow components and stores their relationships, making it more straightforward to audit and verify model provenance.

  3. Set up SageMaker AI Unified Studio. Use Amazon SageMaker AI Unified Studio as your integrated development environment that unifies data and AI workflows. SageMaker AI Unified Studio provides enhanced collaborative features, team sharing capabilities, and visual tools to track the lineage of your ML pipelines, making it more straightforward to understand the relationships between different components across data and AI personas.

  4. Configure SageMaker AI Feature Store. Implement Amazon SageMaker AI Feature Store to create a centralized repository for storing, sharing, and managing features with enhanced feature management capabilities. This purpose-built repository enables you to organize features in a consistent way, making them easily accessible across teams. SageMaker AI Feature Store fosters feature consistency between training and inference phases without requiring additional code, and maintains a record of feature versions over time.

  5. Use SageMaker AI Model Registry. Implement Amazon SageMaker AI Model Registry to catalog models for production, manage model versions, and associate metadata with models. The Model Registry enables lineage tracking by maintaining a history of model versions, approval status, and deployment details. This creates a centralized repository for managing model lifecycles, which facilitates governance and improves adherence to regulations.

  6. Build ML pipelines with SageMaker AI Pipelines. Create reproducible ML workflows using Amazon SageMaker AI Pipelines to automate and standardize the steps in your ML process. SageMaker AI Pipelines allows you to track data history within the pipeline and integrates with SageMaker AI ML Lineage Tracking to analyze input data, its sources, and generated outputs. This integration creates comprehensive lineage tracking across your entire ML workflow.

  7. Implement version control practices. Use version control systems like Git for code, model configurations, and pipeline definitions. Integrate these systems with your lineage tracking to properly link code changes to model versions and training runs. This practice maintains a complete history of how your models have evolved over time.

  8. Establish model attributes for training runs. Use model attributes in SageMaker AI to track specific details about your training runs. This allows you to compare different experiments, understand which parameters led to better model performance, and maintain records of training decisions. For more detail, see Using model attributes to track your training runs on Amazon SageMaker AI.

  9. Implement access controls and auditing. Set up appropriate access controls for your lineage data and implement auditing capabilities to track who accesses or modifies lineage information. Use AWS Lake Formation with SageMaker AI Studio to control and audit data exploration activities, as demonstrated in this example.

  10. Develop regular verification processes. Establish procedures to regularly verify that your lineage tracking system is capturing necessary information for compliance-aligned purposes. Create automated reports that demonstrate the completeness of your lineage tracking to adhere to regulatory requirements.

  11. Foundation model lineage tracking. Consider implementing Amazon Bedrock for tracking lineage in generative AI applications. With foundation models becoming increasingly important, tracking prompt engineering changes, model parameters, and fine-tuning datasets is critical for reproducibility and governance of generative AI systems. Use Amazon Bedrock's governance features to maintain comprehensive lineage tracking when working with foundation models.

Resources

Related documents:

Related examples: