How do you achieve continuous integration and deployment in Azure machine learning?

Utilizing Azure DevOps for automated pipelines is the most effective way to achieve continuous integration and deployment in Azure machine learning. Azure DevOps provides powerful tools that facilitate the automation of the build, test, and deployment processes. This allows for consistent and repeatable workflows, which are essential for integrating new code or changes efficiently and deploying AI models to production environments seamlessly.

Automated pipelines in Azure DevOps streamline the integration of changes made in the codebase and support various tasks such as unit testing, performance testing, and deploying models to different environments. This process fosters a faster development cycle and enhances collaboration among teams, which are key principles of continuous integration and deployment.

In contrast, options that involve human intervention or manual updates do not align with the principles of continuous integration and deployment, which emphasize automation and efficiency. Similarly, creating multiple Azure subscriptions does not contribute to the automation or integration process and would complicate management rather than simplify it.