Why AI Monitoring is Different
AI systems require specialized monitoring beyond traditional application metrics. This module covers comprehensive AI observability.
Key Metrics to Track
Essential metrics for AI system health:
Business Metrics
- Cost per inference
- User satisfaction scores
- Task completion rates
- Revenue impact
- SLA compliance
Implementing Observability
Build comprehensive monitoring infrastructure:
Alerting and Anomaly Detection
Detect issues before they impact users:
Cost Monitoring
Track and optimize AI operational costs:
Dashboard Design
Create effective monitoring dashboards:
- Real-time inference metrics
- Model performance trends
- Cost breakdown by dimension
- Error rate and types
- User interaction patterns
- System resource utilization
Best Practices
- Monitor both technical and business metrics
- Set up proactive alerting
- Track costs continuously
- Implement drift detection
- Maintain historical baselines
- Regular monitoring review meetings