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.

As organizations scale their machine learning platforms from experimentation to production, two operational challenges consistently surface. The first is securing access to sensitive feature data without introducing manual overhead for every new feature group. The second is keeping storage costs predictable when high-frequency streaming workloads generate ever-growing volumes of Apache Iceberg metadata. For example, one retail analytics team discovered that their Apache Iceberg-based offline store had accumulated over 50 TB of metadata files in under a year, driving substantial and unexpected Amazon Simple Storage Service (Amazon S3) charges. Meanwhile, infrastructure teams across industries told us they need Lake Formation-enforced access control on feature data that works automatically at the point of feature group creation. They don’t want it as an afterthought requiring repetitive manual configuration.

Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0 that address these challenges:

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.

These capabilities are delivered through new parameters in the SDK v3 FeatureGroupManager.create() and FeatureGroupManager.update() calls. The LakeFormationConfigtriggers automatic access control setup, and the IcebergProperties configures metadata lifecycle. Both can be set at feature group creation time or applied to existing feature groups.

SageMaker Python SDK v3.8.0, released April 16, 2026, is the foundation for the capabilities described in this post. The modernized SDK introduces a modular architecture, improved performance, and removal of legacy hard dependencies (such as PyTorch). These changes result in faster installation and smaller environments.