Monitoring Strategy and Architecture

Production AI models require continuous monitoring to detect degradation, drift, and failures before they impact business outcomes.

## Why Models Degrade

### Distribution Shift - Covariate Shift: Input feature distributions change over time - Label Shift: Class proportions change (rare events become common) - Concept Drift: The relationship between inputs and outputs changes - Data Drift: New categories or values not seen in training appear

### Real-World Examples - Recommendation model: User behavior shifts after product launch - Fraud model: New fraud patterns not in training data - Credit model: Economic recession changes risk relationships - NLP model: New slang and terminology not in training corpus

## Monitoring Architecture

### Four Pillars of ML Monitoring ``` 1. Data Quality Monitoring - Input data completeness and validity - Statistical distribution checks - Schema validation

1. Model Performance Monitoring
2. - Accuracy, precision, recall, F1
3. - Business metrics (revenue, conversion)
4. - Error rates and failure modes

1. Data/Concept Drift Detection
2. - Feature distribution changes
3. - Prediction distribution changes
4. - Population Stability Index (PSI)

1. System Health Monitoring
2. - Latency and throughput
3. - Error rates and exceptions
4. - Resource utilization
5. ```

## Observability Stack - Metrics: Prometheus + Grafana dashboards - Logging: ELK Stack or CloudWatch Logs - Tracing: OpenTelemetry for request tracing - Alerting: PagerDuty or OpsGenie - ML-specific: Evidently AI, Arize, Fiddler, WhyLogs