You've deployed a model and now need to track how it behaves in production over time. Users are giving feedback on model outputs, and the team wants a clear process for deciding when to adjust thresholds, retrain, or roll back.
How would you monitor model performance after deployment and iterate based on user feedback?
Post-deployment metric monitoringCalibration and score qualityThreshold tuning from recent production dataUsing user feedback as a model improvement signalValidating changes with controlled experiments