SageMaker Model Registry now supports model lineage to improve model governance

Amazon SageMaker Model Registry now supports tracking machine learning (ML) model lineage, enabling you to automatically capture and retain information about the steps of an ML workflow, from data preparation and training to model registration and deployment.

Customers use Amazon SageMaker Model Registry as a purpose-built metadata store to manage the entire lifecycle of ML models. With this launch, data scientists and ML engineers can now easily capture and view the model lineage details such as datasets, training jobs, and deployment endpoints in Model Registry. When they register a model, Model Registry begins tracking the lineage of the model from development to deployment. This creates an audit trail that enables traceability and reproducibility, providing visibility across the model lifecycle to improve model governance.

This capability is available in all AWS regions where Amazon SageMaker Model Registry is currently available except GovCloud regions. To learn more, see view Model Lineage details in Amazon SageMaker Studio.