Cost-Efficient BERT Model Serving on Azure App Service Plan.

Question

Pulumi · Accepted Answer

To serve a BERT model cost-efficiently on Azure, you can utilize an Azure App Service Plan with an appropriate pricing tier and size that balances performance with cost. You'll also need to make use of Azure Machine Learning Services to manage and serve your machine learning models.

The following Python program uses Pulumi to set up an Azure App Service Plan and an Azure Machine Learning Service Online Endpoint, which allows you to deploy and serve your BERT model. Here's how it works:

1. **App Service Plan**: This is the environment for hosting the BERT model serving API. Selecting a cost-efficient tier and size is crucial.
2. **Azure Machine Learning Services**: Create an instance of a machine learning workspace and an online endpoint. The endpoint will be where we serve the BERT model.
3. **Online Deployment**: We deploy the BERT model as an online endpoint which allows for real-time serving of the model.

Before running this program, ensure you have installed the Pulumi CLI, set up your Azure credentials, and configured Pulumi with Azure. You will also need the Python SDK for Pulumi.

```python
import pulumi
import pulumi_azure_native as azure_native

# Azure Resource Group
resource_group = azure_native.resources.ResourceGroup('resource_group')

# Cost-Efficient App Service Plan
app_service_plan = azure_native.web.AppServicePlan("appServicePlan",
    resource_group_name=resource_group.name,
    kind="Linux", # Linux is typically a cost-effective option for serving models
    reserved=True, # This is required for Linux plan
    sku=azure_native.web.SkuDescriptionArgs(
        tier="B1", # B-series are cost-effective options. Choose based on your needs.
        name="B1",
        size="B1",
        family="B",
        capacity=1
    ),
    location=resource_group.location,
)

# Machine Learning Workspace
ml_workspace = azure_native.machinelearningservices.Workspace("mlWorkspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku="Basic", # The 'Basic' plan is generally more cost-effective
    identity=azure_native.machinelearningservices.IdentityArgs(
        type="SystemAssigned",
    ),
)

# Machine Learning Online Endpoint
ml_online_endpoint = azure_native.machinelearningservices.OnlineEndpoint("mlOnlineEndpoint",
    location=resource_group.location,
    resource_group_name=resource_group.name,
    workspace_name=ml_workspace.name,
    kind="Endpoint", # Set up an endpoint for real-time serving
    sku=azure_native.machinelearningservices.SkuArgs(
        name="Standard_DS3_v2",
    ),
    properties=azure_native.machinelearningservices.OnlineEndpointPropertiesArgs(
        # Configuration specific to BERT model serving
        # Given the nature of the BERT model you might adjust the capacity and instance size
        # based on model size and expected request load.
    ),
)

# Export the App Service Plan and Online Endpoint details
pulumi.export('app_service_plan_id', app_service_plan.id)
pulumi.export('ml_online_endpoint_name', ml_online_endpoint.name)
```

This Pulumi program sets up the infrastructure required to host and serve a BERT model in a cost-efficient manner. The program starts by creating a resource group that is a logical container for related resources. Then, it defines an App Service Plan, where you should select a tier that provides a balance between cost and computational resources. We selected the B1 tier as a starting point for cost efficiency.

We then set up an Azure Machine Learning workspace, which is necessary for managing machine learning services such as model training, deployment, and serving. In our case, we make use of an online endpoint for real-time inference with the BERT model. The specific configurations of the endpoint would need to match the requirements of your BERT model.

Finally, we export the IDs of both the App Service Plan and the online endpoint so that you can easily retrieve and manage them later.

Remember, Pulumi automatically tracks dependencies between resources, ensuring they are created, updated, or deleted in the proper order during the deployment process.