1. Automated ML Pipelines with Azure DevOps Service Endpoints.


    To create automated ML pipelines using Azure DevOps, you would typically need to define resources such as service connections, build and release pipelines, variables, and agent pools in Azure DevOps. Service connections (known as Service Endpoints in Pulumi's Azure DevOps provider) are used for authenticating and connecting to external services within your pipeline.

    Here's how you could use Pulumi to create an Azure DevOps service connection that connects to Azure Resource Manager, which you might use to deploy your ML model to an Azure service like Azure Machine Learning:

    Firstly, you'll need the Azure DevOps provider. This allows you to interact with Azure DevOps services to create and manage resources.

    You would then define a ServiceEndpointAzureRM resource using this provider, which represents a service connection to Azure Resource Manager. When setting up your service connection to Azure Resource Manager, you must provide the Azure subscription ID, the subscription name (optional), and the service principal authentication information.

    Below is the Python program that demonstrates this process. Make sure to replace "project_id", "service_principal_id", "service_principal_key", and "tenant_id" with your actual Azure DevOps project ID and Azure Active Directory service principal credentials, which are necessary for authentication.

    import pulumi import pulumi_azuredevops as azuredevops # Create an Azure DevOps Service Connection to Azure Resource Manager. service_endpoint_azure_rm = azuredevops.ServiceEndpointAzureRM("MyAzureRMServiceConnection", project_id="project_id", # Your Azure DevOps project ID. service_endpoint_name="my-azure-rm-service-connection", credentials={ "serviceprincipalid": "service_principal_id", # Your service principal ID. "serviceprincipalkey": { "value": "service_principal_key", # Your service principal secret. "isSecret": True } }, azurerm_spn_tenantid="tenant_id", # Your Azure AD tenant ID. authorization={ "parameters": { "serviceprincipalid": "service_principal_id", "serviceprincipalkey": "service_principal_key", "tenantid": "tenant_id" }, "scheme": "ServicePrincipal" } ) # Export the ID of the service connection. pulumi.export("service_endpoint_id", service_endpoint_azure_rm.id)

    In the above program:

    • ServiceEndpointAzureRM is creating a service connection to Azure Resource Manager.
    • project_id is the ID of your Azure DevOps project.
    • service_endpoint_name is the name you want to give to your service connection.
    • The credentials parameter includes the service principal credentials that Azure DevOps will use to authenticate with Azure.

    Once the service connection is set up, you can use it within your Azure DevOps pipeline to deploy resources to Azure. This could include tasks such as provisioning an Azure Machine Learning environment, training a model, or updating a model's deployment.

    Please remember to handle your credentials carefully and securely, avoiding hardcoding them in your Pulumi program. You can use Pulumi's secret management to keep your credentials secure. Also, ensure that your service principal has the necessary permissions within Azure to perform the tasks that your ML pipeline will require.

    This program can be run directly using Pulumi CLI:

    • First, make sure to install the required Pulumi Azure DevOps provider package. You can use the command pip install pulumi_azuredevops.
    • Then, you can run pulumi up to preview and apply the changes.

    Using Pulumi's infrastructure as code approach, you can programmatically define your DevOps resources, which is helpful for maintaining consistent environments, version controlling your infrastructure, and automating provisioning.