WF-002: Model Monitoring and Maintenance

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Field	Value
WF ID	WF-002
Version	1.0
Status	Active

INFO

Document: Model Monitoring and Maintenance Workflow Version: 1.0 Last Updated: 2023-05-12 Owner: AI Engineering Team

Workflow Overview

The "Model Monitoring and Maintenance" workflow outlines the steps for monitoring the performance of a deployed model and maintaining it over time within the Vertex AI Studio environment. This workflow aims to ensure that the deployed model continues to perform well and remains up-to-date with the latest data and requirements.

Phase 1: Monitoring

Objectives:

• Continuously track the performance of the deployed model
• Detect any significant changes or degradation in model performance

Steps:

1. Set up model performance metrics to be monitored, such as accuracy, precision, recall, and F1-score.
2. Configure Vertex AI's model monitoring capabilities to automatically track the selected performance metrics.
3. Establish thresholds or alert conditions for each metric to identify when the model's performance deviates from the expected range.
4. Continuously monitor the model's performance metrics and track any changes or anomalies.

Exit Criteria:

• Monitoring system is set up and actively tracking the model's performance metrics.
• Thresholds or alert conditions are defined for the monitored metrics.

Phase 2: Evaluation

Objectives:

• Analyze the model's performance and identify areas for improvement
• Determine if the model needs to be updated or retrained

Steps:

1. Regularly review the model's performance metrics and compare them to the established thresholds or expectations.
2. Investigate any significant changes or anomalies in the model's performance.
3. Analyze the model's behavior and identify potential reasons for performance degradation, such as data drift, concept drift, or changes in the problem domain.
4. Determine if the model needs to be updated, retrained, or replaced based on the evaluation findings.

Exit Criteria:

• The model's performance has been thoroughly evaluated, and areas for improvement have been identified.
• A decision has been made on whether the model needs to be updated, retrained, or replaced.

Phase 3: Maintenance

Objectives:

• Update or retrain the model to maintain its performance
• Ensure the model remains relevant and aligned with the latest data and requirements

Steps:

1. If the evaluation phase determined that the model needs to be updated or retrained, create a new version of the model using the latest data and training procedures.
2. Redeploy the updated model to the Vertex AI environment, following the appropriate deployment process.
3. Perform a comparative evaluation between the new and old model versions to validate the performance improvements.
4. Update any relevant documentation, monitoring configurations, or other related artifacts to reflect the changes.

Exit Criteria:

• The updated model has been successfully deployed and is actively monitored.
• The model's performance has been validated, and it meets the required standards.

Success Criteria Checklist

• [ ] Monitoring system is set up and actively tracking the model's performance metrics
• [ ] Thresholds or alert conditions are defined for the monitored metrics
• [ ] Significant changes or anomalies in the model's performance have been identified and investigated
• [ ] Areas for model improvement have been identified based on the evaluation
• [ ] The model has been updated or retrained to maintain its performance
• [ ] The updated model has been successfully deployed and is actively monitored
• [ ] The model's performance has been validated, and it meets the required standards