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This article outlines a minimal feature store implementation using Python, DuckDB, and Redis to resolve training-serving skew and provide low-latency context for LLM agents. By defining features once and serving them in both offline and online formats, the architecture ensures consistency across predictive models and real-time inference pipelines. The approach demonstrates how modern infrastructure must adapt to support both traditional machine learning and the immediate contextual needs of AI agents.

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Feature Stores from Scratch: A Minimal Working Implementation

KDnuggetsJune 11, 20267 min read

Build the five components every feature store needs, then see where AI changes the design.

Most teams discover they need a feature store the hard way. A fraud model works in the notebook and quietly breaks in production. A support agent gives a generic answer because it has no idea who the user is. A recommender pipeline duplicates the same "30-day spend" calculation across three jobs, and two of them disagree. A feature store is the piece of infrastructure that fixes those problems. It defines features once, stores them in two shapes (one for training, one for serving), and keeps both in sync. We are going to build a minimal one from scratch in Python, using DuckDB, Parquet, Redis, and FastAPI. Then we will look at how AI applications change what we actually use it for. The full code is short…

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