Your question is ML System Design in Interviews. 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).
How would you design an ML/NLP/GenAI/MLOps system end to end in a technical interview?
Explain how you would clarify the problem before selecting an architecture. Cover data collection, labeling, model selection, training, deployment, online and batch inference, evaluation, monitoring, retraining, and operational failure modes. Discuss how your design changes for retrieval, ranking, generative, or latency-sensitive systems, including training-serving skew and feature drift.