Start with workload, users, data, quality metrics, latency, throughput, freshness, reliability, and cost targets. Choose a simple baseline, then separate offline data/training/evaluation from online serving and feedback collection.
Make interfaces and ownership explicit; add versioning, access control, monitoring, canary rollout, rollback, and a plan for delayed labels or human review.
Get asked follow-ups live, then receive a scored report — like a real MLE interview loop.
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