This question evaluates competency in end-to-end ML system design, covering problem definition and success metrics, data sources and labeling strategies, feature and model selection, training pipeline and infrastructure, offline and online evaluation, monitoring and alerting, failure modes, privacy/compliance, and scaling and cost trade-offs.
Pick one ML project from your experience and walk through it end-to-end. Be concrete about design trade-offs and numbers.
Cover the following:
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