Feature Store
A centralized library where pre-computed data features are stored and shared across teams and models.
Definition
A centralized system for defining, storing, managing, and serving the features that machine learning models consume. It serves the same feature values to both model training and live prediction, maintains a history for reproducibility, and lets multiple teams discover and reuse features instead of rebuilding them. It typically pairs an offline store for training with a low-latency online store for serving.
Why it matters
A feature store prevents training-serving skew, one of the most common and hardest-to-debug production ML bugs, where a model sees differently computed data in production than it trained on. It also accelerates development by turning feature engineering into shared, reusable infrastructure.
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