datarobot-model-monitoring

# DataRobot Model Monitoring Skill This skill provides comprehensive guidance for monitoring deployed models, tracking performance metrics, detecting data drift, and managing model health. ## Quick Start **Most common use case**: Check deployment health and data drift 1. **Check service stats**: `deployment.get_service_stats(...)` to review prediction volume/latency 2. **Check drift**: `deployment.get_feature_drift(...)` / `deployment.get_target_drift(...)` 3. **Compare over time**: Use `get_service_stats_over_time(...)` and drift periods to assess trends **Example**: "Check the health of deployment abc123 and report any data drift issues" ## When to use this skill Use this skill when you need to: - Monitor model performance in production - Track data drift and feature drift - Detect prediction anomalies - Monitor prediction accuracy over time - Set up alerts for model degradation - Analyze model health metrics - Compare production performance to training performance ## Key capabilities ### 1. Performance Monitoring - Track prediction accuracy and metrics over time - Compare production metrics to training metrics - Monitor prediction volume and latency - Identify performance degradation trends ### 2. Data Drift Detection - Detect changes in feature distributions - Identify feature drift (statistical changes) - Monitor target drift (if actuals available) - Alert on significant drift events ### 3. Prediction Monitoring - Monitor prediction distributions - Detect p...