Feature Store

A feature store is a central system for defining, storing, and serving the input features that machine-learning models consume. A "feature" is a computed signal — a user's 30-day purchase count, average session length, days since signup. The feature store computes these once and makes them available both for training models (offline, in bulk) and for real-time inference (online, low latency). The core problem it solves is training/serving skew: if your training pipeline and your production app compute the same feature slightly differently, the model behaves worse in production than in tests. A feature store enforces one definition used in both places. For SaaS builders adding ML — churn prediction, recommendations, fraud scoring — a feature store is worth it once features are reused across multiple models or teams. Tools include open-source Feast and managed offerings from Tecton and the cloud vendors. Practical note: you likely don't need one for your first model. Start with straightforward pipelines; adopt a feature store when duplication, freshness, and consistency of features become a real bottleneck.