What are the practices for tracking incidents and postmortem ML models?

Practices for incident tracking and post-mortem analysis for ML models include:

• Model performance monitoring: tracking quality metrics (accuracy, precision, recall, F1, etc.) in real-time to detect degradation.

• Data logging: saving input data, predictions, and context for subsequent analysis.

• Root cause analysis: identifying why the model started performing worse — data changes, data drift, bugs in the code.

• Post-mortems: detailed analysis of incidents to identify root causes, document lessons learned, and plan preventive measures.

• Fairness and trust considerations: analyzing incidents from the perspective of model fairness, identifying and eliminating bias.

• Automated alerts: setting up alerts for deviations in key metrics.

• Model and data versioning: enabling rollback to stable versions and understanding what changes affected quality.

These practices help maintain the reliability and transparency of ML systems in production.