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A model can pass its launch tests and still become unreliable once traffic, customer behavior, or upstream data changes. Production performance depends on the entire path from incoming events to delivered predictions, including components that never appeared in the training notebook. An accurate prediction that arrives too late may be as unusable as an incorrect one.
Following a deployed service through release, routine operation, and disruption, this book connects feature pipelines, model versions, latency budgets, and monitoring signals. It distinguishes changing input distributions from changing relationships between inputs and outcomes, then examines what teams can measure when labels arrive slowly. Canary releases, shadow traffic, service objectives, and prediction logs become tools for investigating behavior rather than decorations on a dashboard.
Operational examples address missing features, overloaded endpoints, stale models, and retraining that makes results worse. Readers learn how to define ownership, preserve reproducible artifacts, investigate incidents, and choose between rollback, repair, and replacement. The emphasis is on dependable engineering judgment across model types, with explicit attention to monitoring blind spots and the limits of automatic retraining.
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