Batch write and discover records in Amazon SageMaker Feature Store

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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It provides low-latency online serving for real-time inference, an offline store for historical retention and training feature data, and supports both streaming and batch ingestion patterns. As ML platforms mature, two operational gaps surface repeatedly. First, teams running high-throughput feature pipelines must call PutRecord (which writes a single feature record to the online store) in a loop. This means one API call per record, per feature group, which creates connection overhead and poor throughput. A fraud-detection pipeline ingesting 10,000 records per second across five feature groups must sustain 50,000 individual API calls per second only to keep features current. A second challenge is that teams using the In-Memory storage tier have no way to browse or enumerate records stored in the online store. If record identifiers are lost through a bug or pipeline failure, those records become permanently unrecoverable. There is no offline store for the In-Memory tier to fall back on, no Amazon Athena query to run, and no API to discover what exists. Today, we are announcing two new APIs for Amazon SageMaker Feature Store: BatchWriteRecord — Write up to 25 records across multiple feature groups in a single API call, with partial-success semantics, per-record time-to-live (TTL) control, and the same EventTime-based ordering guarantees as PutRecord. ListRecords — Enumerate record identifiers within a feature group using …

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