1. Azure DevOps for Collaborative AI Research and Development


    To set up Azure DevOps for collaborative AI research and development, we will use Pulumi to create and configure several Azure DevOps resources. These will include creating a new project, setting up a Git repository for source control, and configuring build pipelines for continuous integration (CI). By orchestrating these resources, you can create a robust environment for AI research and development with collaboration and version control at its core.

    Let's go through the main steps you'll take in the Pulumi program:

    1. Create a Project: This is the container for your engineering efforts in Azure DevOps. It hosts repositories, build pipelines, and more.
    2. Initialize a Git Repository: Source control is essential for collaboration. Azure Repos provides Git repositories or Team Foundation Version Control (TFVC) for source control of your code.
    3. Set Up Build Pipelines: Continuous integration (CI) pipelines automate the compiling, testing, and validation of your code changes.

    Here is a full Pulumi program written in Python which creates an Azure DevOps project, a Git repository, and a sample build pipeline:

    import pulumi import pulumi_azuredevops as azuredevops # -- Create a new Azure DevOps Project -- # Documentation: https://www.pulumi.com/registry/packages/azuredevops/api-docs/project/ project = azuredevops.Project("ai-research-project", description="AI Research & Development", visibility="private", version_control="Git", work_item_template="Agile") # -- Set up a new Git Repository in Azure DevOps Project -- # Documentation: https://www.pulumi.com/registry/packages/azuredevops/api-docs/git/ git_repo = azuredevops.Git("ai-research-repo", project_id=project.id, initialization=azuredevops.GitInitializationArgs( init_type="Clean")) # -- Define a Build Definition for CI -- # The CI process compiles code, runs tests, and produces artifacts ready for deployment. # The build pipeline is defined using a YAML file in the repository. # Documentation: https://www.pulumi.com/registry/packages/azuredevops/api-docs/builddefinition/ ci_pipeline = azuredevops.BuildDefinition("ci-build", project_id=project.id, repository=azuredevops.BuildDefinitionRepositoryArgs( repo_id=git_repo.id, repo_type="TfsGit", yml_path="ci-pipeline.yml" # Path to the YAML file within the repository ), ci_trigger=azuredevops.BuildDefinitionCiTriggerArgs( use_yaml=True # Use yaml file for CI pipeline definition ), variables=[ azuredevops.BuildDefinitionVariableArgs( name="BuildConfiguration", value="release" ) ]) # Export the generated project name and repository name for reference pulumi.export('project_name', project.name) pulumi.export('repository_name', git_repo.name)

    This Pulumi program starts by importing the required Pulumi Azure DevOps package. It then creates a new project with an Agile work item template suitable for software development. Within this project, it initializes a new Git repository where your AI codebase will live. Lastly, it defines a build pipeline using a YAML file stored in your repository, instructing Azure DevOps how to build and test your AI models or applications.

    To apply this infrastructure effectively, you'll need to add a ci-pipeline.yml file into your repository that defines your build and test steps specific to your AI projects. This YAML file is a blueprint for Azure DevOps to run the continuous integration processes every time code is pushed to your repository.

    With these resources in place, your team can begin collaborating on AI research and development projects using Azure DevOps tools for project management, source control, and continuous integration.

    Please note that actual AI development practices such as data processing, model training, and validation would require additional configurations and resources, possibly including Azure Machine Learning workspaces, compute resources, and data storage options, which are not covered in this basic setup.