ML System Design

ML System Design is the art of architecting end-to-end machine learning systems that work at scale. It combines software engineering, ML knowledge, and infrastructure expertise to build systems that are reliable, performant, and maintainable.

The typical ML system design interview covers: problem formulation (defining objectives, metrics, constraints), data pipeline (collection, processing, feature engineering), model selection (tradeoffs between approaches), training infrastructure (offline training, online learning), serving architecture (batch vs. real-time, latency requirements), and monitoring/iteration.

Common patterns include recommendation systems, search ranking, fraud detection, content moderation, notification systems, and ad targeting. Key concepts are feature stores, model versioning, A/B testing, canary deployments, and handling scale (millions of QPS). System design questions test your ability to think holistically about ML in production.