Automating Retraining in Azure ML CI/CD Pipeline Based on Data Drift Alerts

Question

I have set up a CI/CD pipeline using Azure Devops for deploying a machine learning model. After deployment, I have scheduled a daily monitoring job to track data drift using production data.

Now, I want to automate the retraining process such that if data drift alerts persist continuously for five days, the retraining pipeline should be triggered automatically.

I would like to know:

1. What is the best approach to implement this in Azure ML?
2. Should I use Azure ML Pipelines, Azure Functions, or another service for this automation?
3. How can I efficiently store and track drift alerts to ensure accurate triggering?
4. Any best practices or recommended workflows for handling automated retraining based on monitoring insights?

Would appreciate any insights or experiences from the community!

Answer 1

Hi Bhaskar Turkar,

Welcome to the Microsoft Q&A Forum! Thank you for your question.

To automate the retraining process based on data drift alerts in Azure Machine Learning, you can follow these steps:

What is the best approach to implement this in Azure ML?

The best approach is to use Azure ML Pipelines combined with Azure Functions. Azure ML Pipelines will help you create and manage the retraining workflow, while Azure Functions can be used to trigger the retraining pipeline based on the data drift alerts.

Should I use Azure ML Pipelines, Azure Functions, or another service for this automation?

• Azure ML Pipelines: Create a pipeline that includes steps for data preprocessing, model training, evaluation, and deployment. You can schedule this pipeline to run automatically when triggered by an external event, such as a data drift alert.
• Azure Functions: Use Azure Functions to monitor the data drift alerts. If the alerts persist for five consecutive days, the function can trigger the Azure ML Pipeline to start the retraining process.

How can I efficiently store and track drift alerts to ensure accurate triggering?

• Use Azure Monitor or Azure Log Analytics to store and track data drift alerts. You can set up alerts and log queries to monitor the drift metrics and trigger actions based on the conditions you define.
• Store the drift metrics in a centralized location, such as an Azure SQL Database or Azure Blob Storage, to ensure accurate tracking and historical analysis.

Any best practices or recommended workflows for handling automated retraining based on monitoring insights?

• Modularize Your Pipelines: Break down your ML pipeline into modular steps, such as data ingestion, preprocessing, training, and evaluation. This makes it easier to manage and update individual components.
• Use MLOps Practices: Implement MLOps practices to ensure reproducibility, scalability, and maintainability of your ML workflows. This includes version control, automated testing, and continuous integration/continuous deployment (CI/CD) pipelines.
• Monitor and Alert: Continuously monitor your models for data drift, performance degradation, and other metrics. Set up alerts to notify you of any issues and trigger automated actions, such as retraining.
• Documentation and Collaboration: Maintain clear documentation of your ML workflows and collaborate with your team to ensure everyone is aligned on the processes and best practices.

For more detailed guidance, you can refer to the official Microsoft documentation:

• Use automated ML in an Azure Machine Learning pipeline in Python.
• Trigger applications, processes, or CI/CD workflows based on Azure Machine Learning events
• Monitor Azure Machine Learning

Hope this will help. Please let us know if any further queries.

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Thanks