Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Artificial Intelligence › Machine Learning & Data

Model Management

concept · part of Machine Learning & Data

Model Management encompasses the processes and tools for versioning, monitoring, and maintaining machine learning models throughout their lifecycle.

Key practices include version control, model registry, monitoring and alerts, and automation of updates.

A model registry is a centralized location to store, manage, and track deployed models, including their metadata and deployment history. It helps manage multiple versions, ensure consistency across environments, and streamline deployment.

After deployment, models need to be monitored for performance metrics such as accuracy, latency, and error rates. Alerts can be set up to notify the team when performance falls below predefined thresholds, enabling timely intervention.

Automating retraining and redeployment of models is a best practice for maintaining model quality. This can be done through scheduled updates or triggered by conditions such as data drift or performance degradation.

• Maintain documentation: keep detailed documentation of each model version, including the data used, the metrics achieved, and the business context. This aids compliance, troubleshooting, and understanding the model's history.

An online retail company uses a recommendation engine to suggest products. After deployment, it is essential to monitor performance metrics like clickthrough rates and user engagement. Over time, the model may become less effective due to changes in customer preferences or new products. Using a model registry and automating retraining ensures the model is regularly updated with new data, maintaining relevance and improving the shopping experience.

Inside Model Management (2)

• Model monitoring — Tracking accuracy, latency, and resource usage, with alerts on threshold breaches to trigger corrective actions.
• Model versioning — Versioning each model iteration to enable redeployment of a specific version.

Connections

• Uses Model versioning
• Uses Model monitoring
• Related to Model versioning
• Related to Model monitoring

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