Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store | Amazon Web Services
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...
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