ML Model Monitoring — Drift Detection & Retraining Alert System

I will build a model monitoring system for your deployed ML model — covering data drift detection (monitoring whether incoming feature distributions have shifted from training distributions), concept drift detection (monitoring whether model prediction accuracy is degrading over time), automated alerting when drift exceeds your defined thresholds, and a retraining trigger pipeline. ML models degrade silently in production as the world changes — customer behaviour shifts, product catalogues update, economic conditions change — and without systematic monitoring, you discover the degradation through business impact rather than through a proactive alert.

The system covers Evidently AI or a custom drift detection implementation, statistical tests (KS test, PSI, or chi-square) for feature distribution monitoring, prediction distribution monitoring, an alerting integration (email, Slack, or PagerDuty), a drift report dashboard, and a retraining trigger when drift exceeds defined thresholds.