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A model shipped bad predictions to production for six hours. Walk me through the incident response.

ML incidents are slipperier than service outages: nothing crashed, the model was just wrong. The strong answer covers detection, mitigation, and a blameless postmortem that fixes the system, not the person.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

ML incidents are slipperier than service outages: nothing crashed, the model was just wrong. The strong answer covers detection, mitigation, and a blameless postmortem that fixes the system, not the person.

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UP NEXT ON YOUR JOURNEY
Next in this trackDesign an evaluation pipeline for an LLM application that runs on every prompt and model change.Next in this trackHow do you build a data flywheel from production feedback, and what makes feedback loops go wrong?Next in this trackWalk me through deploying and scaling model inference on Kubernetes.
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