**Detect data drift, track model performance, and get alerted before your ML models silently degrade in production.**
Browse the actual product documentation and code examples included in this toolkit.
Key features of Model Monitoring Dashboard
• **metric_name**: Which metric to watch • **condition/threshold**: When to fire (e.g., `psi > 0.25`) • **severity**: warning / critical / page • **cooldown_seconds**: Minimum gap between repeat alerts • **description**: Human-readable explanation shown in the alert • **Empirical CDF** via binary search (O(log n) lookup)
**metric_name**: Which metric to watch
**condition/threshold**: When to fire (e.g., `psi > 0.25`)
**severity**: warning / critical / page
**cooldown_seconds**: Minimum gap between repeat alerts
**description**: Human-readable explanation shown in the alert
**Empirical CDF** via binary search (O(log n) lookup)
Configure Model Monitoring Dashboard parameters to see how the product works.
pip install -r requirements.txt python -m src.drift_detectors # Run drift detection demo python -m src.concept_drift # Run concept drift detection demo python -m src.performance_monitor # Run performance monitoring demo python -m src.alert_engine # Run alert engine demo