Which tool is commonly used alongside Azure DevOps for trigger-based automation in ML pipelines?

GitHub Actions is commonly used alongside Azure DevOps for trigger-based automation in machine learning pipelines. It provides a powerful way to create workflows that can be triggered by various events, such as code commits, pull requests, or even scheduled tasks. This capability allows data scientists and machine learning engineers to automate different stages of their ML workflows, from code integration to deployment, thus streamlining the model development and delivery process.

By integrating GitHub Actions with Azure DevOps, teams can effectively manage continuous integration and continuous deployment (CI/CD) cycles for machine learning applications. This integration enhances collaboration among team members, enabling quicker iterations and reduced time to market for machine learning solutions.

The other tools mentioned have distinct functionalities and are not primarily focused on the trigger-based automation aspect for ML pipelines in the same way as GitHub Actions. Kubernetes clusters, for instance, are mainly used for managing containerized applications, while Azure Functions offers serverless computing capabilities for executing code without managing servers, but may not specifically address ML pipeline automation. Azure Notebooks serve as an interactive environment for data science and do not directly provide automation capabilities like GitHub Actions does.