Feature Store Architecture and Design Cheat Sheet

A feature store is a centralized data platform that manages the storage, computation, and serving of machine learning features across training and inference pipelines. It solves the critical challenge of training-serving consistency by ensuring features computed during model training match those used in production predictions. Feature stores distinguish between online stores (low-latency serving for real-time inference) and offline stores (batch retrieval for training), while addressing point-in-time correctness to prevent data leakage. Understanding architectural patterns—literal, physical, or virtual—alongside platform choices like Feast, Tecton, and Hopsworks determines whether your feature store enables true operational ML at scale or becomes another data engineering burden.