Feature Stores from Scratch: A Minimal Working Implementation

A feature store fixes training-serving skew and LLM context problems by defining features once and storing them in offline and online shapes. Five core components handle both predictive ML and LLM personalization use cases. The implementation uses Python, DuckDB, Parquet, Redis, and FastAPI.