In this video, I break down Machine Learning System Design in a super interview-friendly way—using a simple framework you can reuse for most ML system design rounds. I start with how to define the problem statement clearly (what exactly are we optimizing for?), then move to metrics—because you need a way to judge whether your model is good before and after production. I cover offline metrics like precision, recall, and ranking loss, and then online metrics like A/B testing, CTR, and watch time, which is what companies actually care about once the system is live. Then I apply the same principles to a YouTube video recommendation system. I explain why real systems are usually multi-stage: a candidate generation model to quickly fetch relevant videos (embedding-based retrieval + ANN), followed by a ranking model that predicts the probability of a user watching each candidate and sorts them. I also talk about practical requirements like handling imbalanced data (lots of negatives), keeping inference latency under ~100ms, and scaling to millions of users. Finally, I walk through a high-level architecture—databases, a feature pipeline (Spark/MapReduce), model storage (like S3), request flow, and scaling via Kubernetes autoscaling—so you can speak like an engineer in interviews.