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Feature Stores
A feature store is a central place that computes a feature once and serves it to both training (offline, batch) and serving (online, low-latency) from the same definition, which kills the most common production bug in ML: train/serve skew. It also handles point-in-time correctness so backfills do not leak the future. The honest catch is that most early-stage teams do not need one.
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