Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

Amazon SageMaker Feature Store has introduced three new capabilities in its Python SDK v3.8.0. These updates include enhanced support for Lake Formation governance and Iceberg table properties. The improvements aim to accelerate machine learning feature pipelines with practical code examples and notebooks.

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PublishedTuesday, May 19, 2026 at 5:20 PMMay 19, 05:20 PM

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Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table properties in the SageMaker Python SDK repository.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It now supports Apache Iceberg table format, streaming ingestion, scalable batch ingestion, and fine-grained access control through AWS Lake Formation.

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Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

Original article excerpt