Production Prediction Monitoring

A model in production degrades. The data distribution shifts. New patterns emerge that the training data did not contain. Customer behavior changes. Competitive dynamics shift. The model, trained on historical data, progressively represents a reality that no longer exists. Production monitoring tracks three signals: prediction accuracy (does the model still predict correctly?), input distribution (has the data the model receives changed from the data it was trained on?), and prediction distribution (has the output distribution shifted, suggesting the model is seeing something new?). When any signal degrades beyond threshold, the model is flagged for retraining. Without monitoring, degradation is invisible until a decision fails. With monitoring, degradation is caught early and the model is updated before the damage compounds.