Model Monitoring and Drift Detection in Production ML

Deploying a machine learning model is only the midpoint of the lifecycle. Real-world environments evolve continuously, and data distributions shift over time. Without monitoring, model degradation remains invisible until business metrics decline. Drift occurs in two primary forms: data drift and concept drift. Data drift happens when the distribution of input features changes. Concept drift occurs when the relationship between features and labels changes. Monitoring begins with logging structured input features and predictions. Statistical comparisons between training distribution and live data can detect anomalies. Metrics such as KL divergence or population stability index are commonly used. Performance monitoring must track real-world outcomes. Accuracy, precision, recall, or business KPIs should be continuously evaluated against ground truth where available. Alerting systems should trigger when thresholds are exceeded. This ensures proactive retraining rather than reactive debugging. A mature ML system treats monitoring as a first-class citizen. Retraining pipelines, versioning strategies, and rollback mechanisms complete the feedback loop. Without monitoring, models silently fail. With monitoring, they evolve safely.