Implementing CI/CD

CI/CD is a software engineering practice that promotes frequent code integration and automated deployment. This approach is becoming increasingly popular in ML projects to ensure models are constantly improved, validated, and deployed in a streamlined manner. In Azure Machine Learning, there are multiple tools and services to help you implement CI/CD in your ML life cycle.

Here’s an example of CI/CD:

1. By using VS Code with Azure Machine Learning extensions for development, we can develop our scripts.
2. Those scripts can be version-controlled using Git repositories (such as GitHub or Azure Repos).
3. If we have the expertise, we can set up automated testing to validate our models. This might include unit tests, integration tests, and other validation or data checks.
4. We can configure Azure Pipelines to automatically trigger when changes are made to the repository. A CI/CD pipeline could include the following:
   • Training the model
   • Logging metrics...