CI/CD Pipeline for Machine Learning Models with Azure DevOps

Question

Pulumi · Accepted Answer

To set up a CI/CD pipeline for machine learning models with Azure DevOps using Pulumi, you need to define several resources. Below are the resources we'll use, along with their purposes:

Azure DevOps Project: This serves as a container for your application code, builds, releases, and deployments.
Azure Repos Git: Hosts the Git repository for your machine learning code.
Azure DevOps Service Connection: Enables your Azure DevOps Project to connect to external and remote services like Azure Container Registry (ECR).
Azure DevOps Build Pipeline: Automates the building and testing of your code.
Azure DevOps Release Pipeline: Automates the deployment of your machine learning model to various environments.
Azure Machine Learning Compute: A cloud-based compute target that you can use to train your machine learning model or serve your model as a web service.

Below you will find a Python program using Pulumi to create an Azure DevOps project, a service connection to Azure, a Git repository for your code, and a service endpoint to communicate with a machine learning workspace. This setup assumes you're using Azure Machine Learning for training and deploying your machine learning models.

Please note that the program won't cover the Build and Release definitions in detail, as these are more involved and would require knowledge of the model, its dependencies, and the environment it runs in. Those are usually defined in YAML files within your repository and linked to the Azure DevOps pipeline.

import pulumi
import pulumi_azuredevops as azuredevops

# Create a new Azure DevOps project.
project = azuredevops.Project("ml-project",
    description="CI/CD Pipeline for Machine Learning Models",
    visibility="private",
    version_control="Git",
    work_item_template="Agile")

# Azure DevOps Git repository for the Machine Learning code.
git_repo = azuredevops.Git("ml-git-repo",
    project_id=project.id,
    initialization=azuredevops.GitInitializationArgs(
        init_type="Clean"  # Other options like 'Import' might require 'source_type' and 'source_url'.
    ))

# Service connection to Azure, assuming you've set up a service principal.
service_endpoint_azure = azuredevops.ServiceEndpointAzureEcr("service-endpoint-azure",
    project_id=project.id,
    service_endpoint_name="Azure Service Connection", 
    description="Service connection to Azure",
    authorization=azuredevops.ServiceEndpointAzureEcrAuthorizationArgs(
        # The credentials should be obtained from a secure place like Azure Key Vault or Pulumi configuration.
        # This is just a placeholder showing the structure.
        serviceprincipalid="<service_principal_id>",
        serviceprincipalkey="<service_principal_key>",
    ),
    azurecr_subscription_id="<azure_subscription_id>",
    azurecr_subscription_name="<azure_subscription_name>",
    azurecr_name="<azure_container_registry_name>",
    resource_group="<azure_resource_group>",
    azurecr_spn_tenantid="<azure_tenant_id>",
    )

# Now create a service connection to a Machine Learning Workspace. Replace the placeholders with actual values.
# You would get these values from your Azure environment where the workspace is set up.
service_endpoint_ml_workspace = azuredevops.ServiceEndpointAzureEcr("ml-workspace-connection",
    project_id=project.id,
    service_endpoint_name="Azure ML Workspace Connection", 
    description="Service connection to Azure Machine Learning workspace",
    authorization=azuredevops.ServiceEndpointAzureEcrAuthorizationArgs(
        serviceprincipalid="<service_principal_id_for_ml_workspace>",
        serviceprincipalkey="<service_principal_key_for_ml_workspace>",
    ),
    azurecr_subscription_id="<azure_subscription_id>",
    azurecr_subscription_name="<azure_subscription_name>",
    azurecr_name="<azure_ml_workspace_name>",
    resource_group="<azure_resource_group_for_ml_workspace>",
    azurecr_spn_tenantid="<azure_tenant_id>",
    )

# Pulumi export statements for critical information.
pulumi.export("project_name", project.name)
pulumi.export("git_repository", git_repo.name)
pulumi.export("endpoint_azure", service_endpoint_azure.name)
pulumi.export("endpoint_ml_workspace", service_endpoint_ml_workspace.name)

This program sets up the foundational components needed for your CI/CD pipeline in Azure DevOps. After running this program with Pulumi, you will have a new Azure DevOps project and a Git repository ready for your code. You'll also have service connections established, which your pipelines can use to interact with Azure resources.

You'll need to commit your code and define your build (CI) and release (CD) pipelines, which tell Azure DevOps how to build, test, and deploy your machine learning models. These pipeline definitions are usually kept as YAML files within your repository.

For further learning and to dive deeper into creating Azure DevOps build and release pipelines, refer to the Azure DevOps Documentation. For more on using Azure Machine Learning with Pulumi, check out the Azure Machine Learning Compute documentation.