1. Collaborative AI Workflows with Azure DevOps Repositories


    Collaborative AI workflows typically involve source control systems, continuous integration and continuous deployment (CI/CD) pipelines, and resource management. When using Azure DevOps for such workflows, you might need repositories for storing your code, pipelines for automating build and deployment processes, and service connections for interfacing with other services or resources, such as machine learning platforms or data stores.

    In this context, Azure DevOps Repositories provide a Git repository for source control. You can then set up CI/CD pipelines to automatically test, build, and deploy your AI models and applications.

    Below is a Pulumi program in Python that creates an Azure DevOps project, a Git repository within that project, and sets branch policies to protect the main branch. This foundation can then be expanded with build and release pipelines for a complete CI/CD solution.

    import pulumi import pulumi_azuredevops as azuredevops # Create a new Azure DevOps Project project = azuredevops.Project("ai-workflows-project", name="AIWorkflowsProject", description="Project for Collaborative AI Workflows", visibility="private", version_control="Git", work_item_template="Agile") # Create a new Azure DevOps Git Repository within the project repository = azuredevops.Git("ai-workflows-repo", project_id=project.id, name="CollaborativeAIRepo", initialization=azuredevops.GitInitializationArgs( init_type="Clean" # Starts with a clean repository, alternatively you can choose to import code or start with a README )) # Set a branch policy to protect the main branch branch_policy = azuredevops.BranchPolicyBuildValidation("main-branch-policy", enabled=True, blocking=True, branch="main", display_name="Build Validation for Main", project_id=project.id, settings=azuredevops.BranchPolicyBuildValidationSettingsArgs( build_definition_id=0, # Replace with your actual build definition ID valid_duration=72, display_name="Main Branch Build Validation", )) # Export relevant data pulumi.export("project_name", project.name) pulumi.export("repository_name", repository.name) pulumi.export("branch_policy_name", branch_policy.display_name)

    Let me explain the main components of this program:

    1. Azure DevOps Project: azuredevops.Project creates a new project in Azure DevOps. This project is the container for your repositories, pipelines, boards, and more. It's set to be private and use Git for version control.

    2. Azure DevOps Git Repository: azuredevops.Git creates a new repository within the project. This is where your AI workflow's code will be stored. The repository starts clean without any commits.

    3. Branch Policy: azuredevops.BranchPolicyBuildValidation applies a branch policy to the main branch of your repository. This policy ensures that changes to the main branch can only be made if associated build validations (like automated tests or lint checks) pass, ensuring code quality and stability.

    4. Exports: The names of the project, repository, and branch policy are exported. These can be used in the Azure DevOps UI or in other Pulumi programs to reference the created resources.

    This program is a great starting point for setting up your collaborative AI workflows using Azure DevOps. You can extend it with additional branch policies, service connections, and pipelines as needed for your specific AI use cases.