Deploying AI Models as Azure Functions via Azure Portal

Question

Pulumi · Accepted Answer

Deploying AI (Artificial Intelligence) models as Azure Functions is a great way to make your machine learning solutions accessible as web services. Azure Functions is a serverless compute service that enables you to run event-triggered code without having to explicitly provision or manage infrastructure.

In the context of Pulumi and Infrastructure as Code (IaC), we want to automate the deployment process and ensure that our infrastructure is reproducible and version controlled. We'll be using Pulumi's Python SDK to define and deploy our AI model as an Azure Function.

Here's what we are going to do:

1. Define a resource group which acts as a logical container for our Azure resources.
2. Create a storage account required by Azure Functions to operate.
3. Set up an Application Insights resource for monitoring the function's performance.
4. Create a function app service where our AI model will be hosted.
5. Deploy the AI model files as part of the Function App code using a zip deployment method.

Below is a Pulumi program written in Python that sets up the necessary resources in Azure and deploys an AI model as an Azure Function.

```python
import pulumi
import pulumi_azure as azure

# Create an Azure Resource Group
resource_group = azure.core.ResourceGroup("ai_model_resource_group")

# Create an Azure Storage Account required for Azure Functions
storage_account = azure.storage.Account("aimodelstorageaccount",
    resource_group_name=resource_group.name,
    account_replication_type="LRS",
    account_tier="Standard")

# Create an Application Insights instance for the AI model
app_insights = azure.appinsights.Insights("aiModelAppInsights",
    resource_group_name=resource_group.name,
    application_type="web")

# Create an Azure Function App Service for the AI Model
function_app = azure.appservice.FunctionApp("aiModelFunctionApp",
    resource_group_name=resource_group.name,
    app_service_plan_id=service_plan.id,
    storage_account_name=storage_account.name,
    storage_account_access_key=storage_account.primary_access_key,
    app_settings={
        "APPINSIGHTS_INSTRUMENTATIONKEY": app_insights.instrumentation_key,
        "FUNCTIONS_WORKER_RUNTIME": "python"  # Assuming we have a Python AI model
    },
    version="~3")  # Indicating the runtime version of Azure Functions

# Use `archive` to deploy our AI model as a Function App using zip deployment
# Here we assume the actual AI model code, pre-configured for Azure Functions, is located in `./functionapp`
# Make sure that directories for function apps include a `host.json` and each function has a `function.json`
app_archive = pulumi.asset.FileArchive("./functionapp")
function_app_code = azure.appservice.FunctionAppSourceControl("aiModelSourceControl",
    resource_group_name=resource_group.name,
    function_app_name=function_app.name,
    branch="main",
    repository_url="https://github.com/your-repo/your-model",
    is_manual_integration=True)

# Export the Function App URL so we can access the function once it's deployed
pulumi.export('endpoint', function_app.default_hostname.apply(
    lambda hostname: f"https://{hostname}/api/{function_name_here}"))

```

Here's what each part of the code does:

- We first create an Azure Resource Group, it's a logical container where all the Azure resources will reside.
- Then we create an Azure Storage Account because Azure Functions need an Azure storage account for operations such as managing triggers and logging function executions.
- We set up an Application Insights resource that helps you in monitoring the performance and errors of your AI model function app.
- Next, we create an Azure Function App that serves as a hosting environment for your AI model.
- Inside the Function App, we set `FUNCTIONS_WORKER_RUNTIME` to Python, assuming the deployed AI model is Python-based.
- Lastly, we tell Pulumi how our code is organized for deployment. In this case, it's deployed through a link to a GitHub repository where our Azure Functions code for the AI model lives. Ensure that this code is ready to be run with Azure Functions, and that there exists a `host.json` file and `function.json` for each function.

Replace `your-repo/your-model` with your actual GitHub repository URL and ensure that your AI model is properly configured as an Azure Function in the `./functionapp` directory.

This Pulumi program will provision the necessary infrastructure and deploy the AI model all at once, giving you a repeatable and version-controlled way to manage your cloud resources and code.

Remember to replace placeholder values like `your-repo/your-model` and `function_name_here` with your actual data. The `function_name_here` should match the name of the function that you expose from your deployed AI model in Azure Functions.