midMLOps
What is a feature store in MLOps?
Updated May 17, 2026
Short answer
A feature store centralizes storage and serving of ML features for training and inference.
Deep explanation
A feature store ensures consistency between offline training and online serving by maintaining versioned, reusable features. It reduces duplication, prevents training-serving skew, and improves governance. It typically has offline (batch) and online (low-latency) stores.
Real-world example
E-commerce platforms reuse user behavioral features across recommendation and fraud models.
Common mistakes
- Computing features separately for training and production.
Follow-up questions
- What is offline vs online feature store?
- Why is it important for scaling?