Amazon SageMaker Unified Studio Powers Governed ML Feature Store With Time-Travel Queries and Cross-Team Data Sharing

Amazon SageMaker Unified Studio now powers a governed ML feature store with time-travel queries, enabling data engineers to publish versioned feature tables while data scientists securely discover, reuse, and query historical snapshots for reproducible model training across teams.

• Amazon SageMaker Unified Studio and SageMaker Catalog are being used to build a governed offline feature store that enables data engineers to publish versioned ML feature tables and data scientists to discover, subscribe to, and reuse them for model training.
• The solution leverages Amazon S3 Tables with Apache Iceberg for ACID-compliant, time-travel-capable feature storage, while AWS Lake Formation enforces fine-grained access control and a publish-subscribe pattern streamlines secure cross-team asset sharing.
• Data scientists can query historical feature snapshots for reproducible model training, track full data lineage, and run batch inference pipelines using SageMaker XGBoost, with experiment parameters and metrics logged to MLflow for traceability.