Auto-Scaling Environments for Real-Time AI Inference

Question

Pulumi · Accepted Answer

When building an auto-scaling environment for real-time AI inference, we want to ensure that our infrastructure can dynamically adjust to the load. For this, we would typically need an environment that is capable of serving real-time AI models, along with monitoring capabilities to scale the infrastructure in or out based on the demand.

In Azure, you can use Azure Machine Learning (AML) for serving real-time predictions using machine learning models. Azure Machine Learning supports deploying models as web services that automatically scale based on the number of incoming prediction requests. You can configure auto-scaling settings, including the minimum and maximum number of nodes to scale out or in depending on the CPU or memory usage.

To build such an environment, we will use Pulumi to define our infrastructure as code. Pulumi enables us to write code in languages like Python to describe cloud resources, which Pulumi then deploys and manages.

Here's how you would do that with Pulumi and Azure Machine Learning:

Create an Azure Machine Learning workspace.
Register the environment that hosts the machine learning model.
Deploy the model into an inference endpoint, which will be exposed as a web service.
Configure the autoscaling settings for the inference endpoint.

Before starting, make sure you have Pulumi installed and configured with your Azure credentials. Now let's dive into the Pulumi code to create an Auto-Scaling Environment for Real-Time AI Inference on Azure:

import pulumi
from pulumi_azure_native import machinelearningservices as aml
from pulumi_azure_native import resources

# First, we create a resource group where our resources will live.
resource_group = resources.ResourceGroup('ai-inference-rg')

# Create an Azure Machine Learning workspace.
workspace = aml.Workspace(
    "ai-inference-workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    identity=aml.IdentityArgs(
        type="SystemAssigned"
    ),
    sku=aml.SkuArgs(
        name="Basic" # Choose the appropriate SKU for your production needs.
    )
)

# Register the machine learning environment.
environment_container = aml.EnvironmentContainer(
    'ai-inference-environment-container',
    resource_group_name=resource_group.name,
    workspace_name=workspace.name
)

# Deploy the model to an online endpoint (web service for real-time inference).
# Here we assume you've already registered the model and created an entry in the sklearn environment.
online_deployment = aml.OnlineDeployment(
    'ai-inference-online-deployment',
    resource_group_name=resource_group.name,
    workspace_name=workspace.name,
    endpoint_name="realtime-inference-endpoint",
    deployment_name="realtime-inference-deployment",
    online_deployment_properties=aml.OnlineDeploymentPropertiesArgs(
        scoring_uri="http://<model-scoring-uri>", # Replace with your model's scoring URI.
        swagger_uri="http://<model-swagger-uri>"   # Replace with your model's swagger URI.
        # Configure auto-scaling settings
        autoscale_configuration=aml.AutoscaleConfigurationArgs(
            min_replica_count=1,
            max_replica_count=3
        )
    )
)

# Output the Web service URI once the deployment is completed.
pulumi.export('inference_endpoint', online_deployment.scoring_uri)

Explanation of the code and resources:

ResourceGroup sets up a container for all your resources.
Workspace creates an instance for Azure Machine Learning, where you can deploy and manage machine learning models.
EnvironmentContainer is configured to manage the machine learning environments. You would need to set up an environment specifically for your model with the necessary dependencies.
OnlineDeployment represents the deployment of the machine learning model. The code above assumes that you have a model scoring URI and a Swagger URI. The auto-scaling settings are included in this deployment, specifying the min_replica_count and max_replica_count.

Remember to replace <model-scoring-uri> and <model-swagger-uri> with the actual URIs for your model. After running this Pulumi program, your AI inference service will automatically scale within the specified range to provide real-time predictions.

Keep in mind that this is a basic setup and will need enhancements for security, monitoring, and possibly cost management for production environments. Additionally, the actual machine learning model and environment details are not included in this example as they would need to be developed according to your specific AI use case.