In this video, I design a Spotify-like music recommendation engine end-to-end, but from a production ML system design lens—not just “here’s a model.” I start with problem formulation and requirements: what “good recommendations” actually mean, what latency and scale constraints look like, and how you should think about offline vs online evaluation (because optimizing a metric in a notebook is very different from moving user retention in the real world). Then I break the architecture into the core blocks you’ll see in modern recommenders: a retriever (candidate generation), a ranker, and often a re-ranker. I walk through common model choices like Two-Tower retrieval, DeepFM-style ranking, and transformer-based recommenders, and how these fit into batch + real-time pipelines. I also cover the systems glue that makes this work at scale: Kafka/Flink for streaming, feature stores, caching (Redis), vector search, and MLOps/observability with tools like MLflow, Prometheus, and Grafana. Finally, I discuss cold-start and hybrid approaches, and the real trade-offs between accuracy, cost, explainability, and maintainability—because that’s the part most “recommender” videos skip.