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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.

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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