ML Model Monitoring — Detecting Drift and Degradation in Production

An ML model in production degrades. The world changes, data changes, and a model trained 6 months ago no longer reflects reality. Without monitoring, you find out when customers start complaining.

Types of Drift¶

• Data drift — the distribution of input data changes (new customer segment)
• Concept drift — the relationship between features and target changes (behavior shift)
• Prediction drift — the distribution of predictions changes

Evidently AI for Drift Detection¶

Evidently — an open-source framework for ML monitoring. Generates drift reports, compares production data with training data. Integration with a Grafana dashboard — alerting when thresholds are exceeded.

Automatic Retraining Pipeline¶

Drift detected → Airflow triggers the retraining pipeline → new model in MLflow → automatic evaluation → if better → Staging → manual approval → Production. The entire cycle in under 4 hours.

Deploying a Model Isn’t the End — It’s the Beginning¶

An ML model without monitoring is a silent disaster. Drift detection + automatic retraining = a sustainable ML system.