ML System Design Case Studies

Knowing algorithms is not the same as designing a system that uses them. A real ML system starts with a decision the business needs to make, a metric that says whether it was made well, and labels that arrive late, noisy or biased. Only then come features, a model, a serving path with a latency budget, and the monitoring that keeps it honest after launch.

Each lesson in this module designs one well-known system the way an ML system design interview or a real design review would: problem framing, requirements and estimates, data and labels, features, model choice, a glowing serving architecture, deep dives into the hardest parts, evaluation and A/B testing, monitoring and failure modes. You will design fraud detection, ETA prediction, feed and search ranking, ad click prediction, content moderation, demand forecasting and visual search, and you will see the same few patterns, such as rules plus models, candidate generation plus ranking, and fast signals plus slow labels, come back in different forms.