plan-monitoring

Model monitoring and maintenance planning defines how to track deployed model performance, detect data drift, trigger alerts, and schedule retraining. It ensures production models remain accurate and governed throughout their lifecycle.

To create a monitoring plan for drift detection and alerting, analyze existing deployment documentation to identify gaps, then generate a structured plan covering input features, labels, prediction tracking, and retraining triggers.

Yes, you can automate deployment monitoring documentation by generating a ready-to-edit plan document at docs/crisp-dm/6-deployment/6.2-plan-monitoring.md, which references prerequisite documents and outlines governance across deployment architectures.

A model maintenance plan should include drift detection monitoring across input features and labels, prediction governance across environments, alerting configurations, and retraining triggers to address data quality degradation.

Yes, this monitoring plan approach works across different deployment architectures and data sources, providing governance and prediction monitoring coverage regardless of the specific production environment setup.

You need to plan model retraining triggers when maintaining a production model and detecting performance degradation or data drift, ensuring the deployed model receives timely updates to preserve prediction accuracy.