Feature stores centralize the storage, management, and serving of ML features for training and inference consistency. Core problem: Features computed differently in training vs serving leads to training-serving skew. Feature logic duplicated across teams. Key capabilities: Feature registry: Catalog of available features with metadata. Offline store: Historical features for training (data warehouse, parquet). Online store: Low-latency feature retrieval for inference (Redis, DynamoDB). Feature serving: APIs to fetch features by entity ID. Transformation: Feature engineering pipelines, consistent transformation. Benefits: Reuse features across models, ensure consistency, reduce redundant computation, enable discovery. Architecture: Transform raw data into features, store in offline/online stores, serve to training and inference. Popular options: Feast (open source), Tecton (commercial), Vertex AI Feature Store, Databricks Feature Store, SageMaker Feature Store. Entity concept: Features organized by entity (user_id, product_id). Fetch features by entity key. Time travel: Retrieve historical feature values as they were at specific times for accurate training. Essential infrastructure for production ML at scale.