Mid-level interview questionAsked at Capital One · interview write-up
You have trained a fraud model: how would you deploy it with versioning and safe rollbacks, monitor it after launch, and keep inference latency low?
Model answer
Ship model, feature code and threshold as one versioned artifact in a registry. Run it in shadow mode, then canary it on a slice of traffic, with one-step rollback to the previous version.
Monitor latency, score distribution and feature drift, and precision and recall once labels arrive. For low latency: precomputed features, a compact model served in-process or on an optimised runtime, and a rules fallback if the model times out.
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