System Design Article

ML System Design (Feature Store, Model Serving)

Difficulty: Hard

An ML system in production is mostly a data system with a model in the middle. The model is the smallest, most-discussed, and least-troublesome part. The hard parts are training data pipelines, feature freshness and parity between training and serving, the feature store that enforces that parity, model deployment and rollback, online and offline evaluation, and the operational concern that the model silently degrades as the world drifts. This lesson covers the canonical reference architecture: training pipeline, feature store with online and offline halves, model registry, serving infrastructure, monitoring, and the feedback loop. It is the senior-level mental model for designing 'add ML to product X' without falling into the standard traps.