Monitoring Deployed Models
Models require monitoring after deployment.
Performance Monitoring
Track prediction distributions. Compare to training. Alert on distribution shifts.
Log predictions with timestamps. Dashboard visualization.
Data Quality
Validate input data schemas. Check for missing values. Monitor feature distributions.
Data quality issues often precede model degradation.
Retriggering
Monitor for performance drop. When detected, retrain on new data.
Schedule regular retraining even without detected issues.
Key Takeaways
- Monitor prediction distributions for drift
- Validate input data quality
- Establish retraining triggers and schedules