Continuous Deployment of AI Models on Azure Web Services

Question

Pulumi · Accepted Answer

Continuous Deployment (CD) of AI models on Azure is a process that allows you to update your machine learning models automatically as new code is pushed to your repository. This enables a seamless transition from development to production with minimal downtime. In Azure, this can be typically achieved by using Azure Machine Learning (AML) Services to manage the models, and Azure Web Services or Azure Kubernetes Service (AKS) for hosting them.

In the steps below, we will assume that the AI model is already trained, registered, and stored in Azure Machine Learning Service Workspace. We will use the Pulumi Python SDK for Azure to set up the infrastructure required for Continuous Deployment of the AI model via a web service.

Here's how you can use Pulumi to automate the deployment of AI models to Azure Web Services:

1. **SetUp an Azure Machine Learning Workspace**: The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create.

2. **Create a Web Service**: You deploy your model as a web service that can be consumed by other applications. For simplicity, we'll use Azure's native web service for machine learning.

3. **Configure CI/CD**: This step involves setting up a Continuous Integration and Continuous Deployment (CI/CD) pipeline, usually with Azure DevOps, that can automatically deploy the model to the web service whenever changes are made.

Below is a Pulumi program that uses the `azure-native` Pulumi provider to set up an Azure Machine Learning Web Service using Python. This program does not set up the complete CI/CD pipeline, but it does the necessary setup for the Machine Learning Web Service, which is a central part of the CD process for AI models.

```python
import pulumi
import pulumi_azure_native as azure_native

# Initialize a resource group for our infrastructure
resource_group = azure_native.resources.ResourceGroup("ai-cd-resource-group")

# Setup an Azure Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace(
    "ml-workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    workspace_name="my-ml-workspace",
    sku=azure_native.machinelearningservices.SkuArgs(name="Standard"),
    identity=azure_native.machinelearningservices.IdentityArgs(
        type=azure_native.machinelearningservices.ResourceIdentityType.SYSTEM_ASSIGNED,
    ),
)

# Deploy a machine learning model as a web service
web_service = azure_native.machinelearning.WebService(
    "ml-web-service",
    resource_group_name=resource_group.name,
    location=ml_workspace.location,
    web_service_name="my-ml-service",
    properties=azure_native.machinelearning.WebServicePropertiesArgs(
        machine_learning_workspace=azure_native.machinelearning.ResourceIdArgs(
            id=ml_workspace.id,
        ),
        # Use the actual model and assets specifications according to your requirements here.
        # This part of the configuration depends on how your AI model and environment are packaged.
    ),
)

# Export the Web Service URL, which can be used to access the AI model
pulumi.export("web_service_url", web_service.properties.apply(lambda prop: prop.scoring_uri))
```

This is a basic setup. A CI/CD process involves more components such as source control, build and release pipelines, which are not covered by this program. With Azure, a typical approach would be to use Azure DevOps for the CI/CD pipeline which would automatically build your code (e.g., a new version of your machine learning model) and deploy it to this web service when triggered by a Git push or a pull request merge.

For detailed instructions on setting up a CI/CD pipeline, you would typically follow Azure DevOps documentation to set up build and release pipelines that connect your source code repository with your Azure services.

Remember that this code will create Azure resources which can incur costs. Be sure to understand the pricing and clean up resources when not in use to avoid unnecessary charges.