Feature engineering consumes substantial ML development time, often duplicated across teams. Feature stores centralize feature definitions, computation, and serving. This centralization ensures training-serving consistency while enabling feature reuse across models.
Core Capabilities
Feature stores provide feature registry documenting available features. Offline stores support batch training with historical data. Online stores serve features with low latency for inference. Feature computation pipelines maintain freshness automatically.
- Feast provides open-source feature store capabilities
- Tecton offers managed feature store with advanced capabilities
- Cloud providers offer integrated feature stores—Vertex AI, SageMaker
- Consider point-in-time correctness preventing data leakage in training
- Balance feature freshness against computation and storage costs
Implementation Patterns
Start with high-value features used across multiple models. Implement feature monitoring detecting drift and quality issues. Version features enabling model reproducibility. Design feature pipelines for incremental updates rather than full recomputation.