Your question is End-to-End ML System Design. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Design an ML system covering data pipeline, model selection, training strategy, evaluation, deployment, scalability, and monitoring.
Asked in the Round 3 ML System Design stage. Focus on production scalability, offline and online training, and latency considerations.
Your answer should cover how you would build the full lifecycle, from data ingestion to model monitoring in production. Be explicit about tradeoffs, failure modes, and how you would prevent training-serving skew.