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ML Feature Store Basics

Centralize and serve ML features

A feature store standardizes feature definitions, computes them in batch/stream, and serves them consistently for training and online inference with lineage and quality monitoring.

Define reusable features

Create documented feature definitions with owners and tests.

Handle offline and online

Store historical features for training and serve low-latency versions for inference.

Ensure correctness

Use point-in-time joins to avoid leakage; validate freshness SLAs.

Monitor and govern

Track drift, staleness, and access; deprecate unused features.

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