Model Monitoring

Model monitoring is the practice of continuously observing a deployed machine learning model's inputs, outputs, and performance metrics in production to detect degradation, drift, and unexpected behavior. It includes tracking statistical properties of input data, monitoring prediction distributions, comparing model outputs against ground truth labels when available, and setting alerts for threshold violations.

Effective model monitoring is essential for maintaining AI system reliability in production commerce environments. Without it, a model that silently degrades — due to data drift, upstream data pipeline changes, or concept drift — may continue producing outputs that look structurally valid but are substantively wrong. Production monitoring systems should instrument models at multiple levels: input data quality, output confidence, downstream business metrics, and system health. Monitoring data also drives retraining decisions, creating a closed feedback loop between production behavior and model improvement.