DriftWatch

Monitor feature distributions for drift that degrades ML model performance

Data & AnalyticsFreemium

DriftWatch continuously monitors the statistical distributions of ML model input features in production, detecting covariate shift, concept drift, and prior probability shift before they degrade model performance. The tool uses Kolmogorov-Smirnov tests, Population Stability Index, and Jensen-Shannon divergence to quantify drift severity and correlates detected drift with model accuracy changes. ML platform teams use it to trigger model retraining only when drift is likely to impact predictions, avoiding both stale models and unnecessary retraining cycles.

Key Features

• ✓Multi-test drift detection
• ✓Drift-accuracy correlation
• ✓Retrain trigger automation
• ✓Feature-level monitoring
• ✓Historical drift timeline