Model Monitoring and Drift Detection

Create a comprehensive ML model monitoring system.

Monitoring requirements:
- Model type: [YOUR MODEL]
- Deployment: [REST API/BATCH/STREAMING]
- Data volume: [PREDICTIONS PER DAY]
- Alert channels: [EMAIL/SLACK/PAGERDUTY]

Monitoring components:
1. Data monitoring:
   - Feature distribution tracking
   - Statistical drift tests (KS, PSI)
   - Missing value patterns
2. Prediction monitoring:
   - Prediction distribution
   - Confidence scores
   - Output anomalies
3. Performance monitoring:
   - Delayed ground truth handling
   - Proxy metrics
   - A/B test monitoring
4. Infrastructure monitoring:
   - Latency tracking
   - Throughput
   - Error rates
5. Drift detection:
   - Concept drift
   - Data drift
   - Covariate shift
6. Alerting:
   - Threshold configuration
   - Alert routing
   - Runbook integration
7. Dashboard:
   - Real-time metrics
   - Historical trends
   - Investigation tools

Integrate with Evidently, WhyLabs, or custom.