Course Description
System design for production machine learning. The ML project lifecycle: problem framing, data collection, modeling, evaluation, deployment, monitoring. Common failure modes: training-serving skew, data drift, concept drift, silent failures. System design interviews for ML roles. Case studies: recommendation systems, search ranking, fraud detection, content moderation. Trade-offs: online vs. batch inference, model complexity vs. latency, precision vs. recall. Students design the architecture for a complete ML system end-to-end.