Monitoring AI Systems in Production
Production AI requires continuous monitoring. Track performance, detect drift, alert on failures, and maintain quality over time.
TL;DR
Monitor AI systems continuously for performance degradation, data drift, cost spikes, and user satisfaction. Alert on anomalies and have runbooks for common failures.
What to monitor
Performance metrics:
- Latency (response time)
- Error rate
- Throughput (requests/sec)
- Availability (uptime)
Quality metrics:
- Accuracy, precision, recall
- User ratings/feedback
- Task success rate
Cost metrics:
- Token usage
- API spend
- Per-user cost
Usage patterns:
- Query volume
- Popular features
- Geographic distribution
Data drift
What is it?
- When real-world data changes from training data
- Model performance degrades over time
Types:
- Feature drift: Input distribution changes
- Concept drift: Relationships change
Detection:
- Compare input distributions over time
- Track performance trends
- Statistical tests (KS test, chi-square)
Response:
- Retrain model
- Update training data
- Adjust prompts
Setting up monitoring
Logging:
- Log all requests and responses
- Include metadata (user, timestamp, model version)
- Sample or aggregate for cost
Dashboards:
- Real-time metrics
- Historical trends
- Segment by user, feature, region
Alerts:
- Error rate spikes
- Latency increases
- Cost anomalies
- Negative feedback surge
Alert thresholds
Set carefully:
- Too sensitive: Alert fatigue
- Too loose: Miss real issues
Start with:
- Error rate > 5% (adjust based on baseline)
- p95 latency > 3x normal
- Daily cost > 20% above average
Response runbooks
High error rate:
- Check API status
- Review recent changes
- Rollback if needed
- Escalate to on-call
Performance degradation:
- Check model drift
- Review recent data
- Retrain or adjust prompts
Cost spike:
- Identify source (user, feature)
- Check for abuse
- Rate limit if needed
Continuous evaluation
Automated testing:
- Run eval suite regularly
- Catch regressions early
Human review:
- Sample outputs weekly
- Qualitative assessment
- Discover edge cases
User feedback:
- Thumbs up/down
- Detailed surveys
- Support ticket analysis
Tools and platforms
APM tools:
- Datadog, New Relic, Grafana
ML-specific:
Custom:
- Build on your stack
- More control, more effort
Best practices
- Monitor from day 1
- Set up alerts before incidents
- Review dashboards weekly
- Update thresholds as you learn
- Document common issues and fixes
What's next
- Responsible AI Deployment
- A/B Testing AI
- Incident Response for AI
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