You have a model that performs well offline, but the real concern is whether it will hold up on future operational data. Describe how you would evaluate it before launch and monitor it after deployment so degradation on unseen data is detected early and handled safely.
Generalization gap between training, validation, and recent holdout dataCalibration drift and threshold instabilitySegment-level regressions hidden by aggregate metricsOperational impact of false positives and false negatives