1. Managing AI Project Workflows with Azure DevOps Services


    Managing AI project workflows can be quite complex, involving various tasks such as source control, continuous integration, deployment, and monitoring. Azure DevOps Services offers a set of tools that can help in setting up and automating these workflows for AI projects.

    In the context of using Pulumi to manage infrastructure as code, you would typically define the necessary Azure DevOps resources, like service connections to Azure, build and release pipelines, repositories, and any other Azure services that are part of your AI workflow, such as Azure Machine Learning workspaces, Azure Kubernetes Service (AKS) clusters for model deployment, etc.

    Below you will find a Pulumi program that sets up an Azure DevOps project with a service connection to an Azure Resource Manager, necessary for deploying resources to Azure. This is just one part of the workflow, focusing on the infrastructure setup stage. The actual build and release definitions would also need to be defined either as code or within Azure DevOps Service, depending on your project requirements.

    import pulumi import pulumi_azuredevops as azuredevops import pulumi_azure_native as azure_native # Set up a new Azure DevOps Project devops_project = azuredevops.Project( "ai-project", name="AIProject", description="Project to manage AI workflows", visibility="private", version_control="Git", work_item_template="Agile" ) # A service connection to Azure Resource Manager will be needed for deployments service_endpoint = azuredevops.ServiceEndpointAzureRM( "ai-service-endpoint", project_id=devops_project.id, service_endpoint_name="ai-service-endpoint-azure", credentials={ "serviceprincipalid": azure_native.ClientConfig.get().client_id, "serviceprincipalkey": azure_native.ClientConfig.get().client_secret, }, azurerm_spn_tenantid=azure_native.ClientConfig.get().tenant_id, azurerm_subscription_id=azure_native.ClientConfig.get().subscription_id, ) # Export the Azure DevOps project name pulumi.export("project_name", devops_project.name) # Export the Service Endpoint (Service Connection) ID for reference pulumi.export("service_endpoint_id", service_endpoint.id)

    In the above program, we start by importing the necessary Pulumi modules for Azure DevOps and Azure Native. We then create a new Azure DevOps project for our AI workflows, wherein we specify project details like name, description, visibility, version control, and work item template.

    Next, we set up a service endpoint within Azure DevOps that connects to Azure Resource Manager. This connection will be necessary to deploy resources to Azure. For this, we're using the Azure DevOps Service Endpoint Azure Resource Manager resource. Credentials for the service principal (used to authenticate and authorize the operation) are sourced from the Pulumi Azure Native ClientConfig.

    Finally, the program exports the project name and the service endpoint ID. These exports make it possible to quickly grab vital information about the resources, which can be handy for future operations or integration with other steps in your workflow.

    With this Pulumi program, you have laid the groundwork for an AI project workflow within Azure DevOps. Further steps would include setting up build and release pipelines for your AI project, which could involve compiling code, training models, running tests, and ultimately deploying the AI solution to a target environment like AKS.