Guides ยท Technology
Model Monitoring (MLOps) Basics
Watch models in production
Monitoring deployed models involves capturing predictions and outcomes, tracking metrics like accuracy and drift, watching input data quality, and alerting when thresholds break to trigger retraining or rollback.
- mlops
- model monitoring
- drift
- data quality
- alerts
Collect
Log inputs, predictions, and outcomes with IDs for auditing.
Track Metrics
Monitor performance, drift, and feature data quality over time.
Respond
Alert on thresholds; retrain, recalibrate, or rollback as needed.
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