Feature Stores and Training-Serving Skew

A feature store computes features once and serves them to both training (offline, historical) and serving (online, low-latency) from the same definitions, which is the fix for training-serving skew, the silent bug where features are computed differently in training and production and the model degrades. It also enforces point-in-time correctness to prevent leakage. Applied-AI interviews probe it because training-serving skew is one of the most common, hard-to-debug production ML failures, and the feature store is the systemic answer.