1. Auto-Scaling Infrastructure for Real-Time AI Inference Services


    Creating an auto-scaling infrastructure for real-time AI inference services is a common scenario in the modern cloud landscape. It allows you to serve your machine learning models efficiently by automatically adjusting the compute resources based on the incoming request traffic, ensuring that you're not over or under-provisioning your infrastructure, which can either lead to unnecessary costs or poor user experiences due to latency.

    For this tutorial, I'll guide you through creating an auto-scaling infrastructure on Azure, using Azure Machine Learning Services and an Inference Pool, which can auto-scale depending on the load. This example assumes that you already have a machine learning model and you want to deploy it for real-time inference.

    Below is a Pulumi program in Python to set up such an infrastructure:

    1. Azure Machine Learning Workspace: A central resource in Azure Machine Learning service that provides a space where you can experiment, train, and deploy your machine learning models.

    2. Azure Machine Learning Inference Pool: A pool of compute resources for deploying and serving machine learning models. It allows auto-scaling to automatically adjust the number of compute instances based on the inference load.

    3. Azure Machine Learning Inference Endpoint: An endpoint where your model is deployed. It's the URL that your applications can use to access the AI inference service.

    Now, let's define the infrastructure:

    import pulumi import pulumi_azure_native.machinelearningservices as machinelearningservices # Define the Azure resource group resource_group = machinelearningservices.ResourceGroup("ai_resource_group") # Create an Azure Machine Learning Workspace workspace = machinelearningservices.Workspace("ai_workspace", resource_group_name=resource_group.name, location="East US", sku=machinelearningservices.SkuArgs(name="Standard"), description="Workspace for AI services", ) # Create an Inference Pool that can autoscale inference_pool = machinelearningservices.InferencePool("ai_inference_pool", resource_group_name=resource_group.name, workspace_name=workspace.name, location=workspace.location, sku=machinelearningservices.SkuArgs(name="Standard_D3_v2", tier="Standard"), inference_pool_properties=machinelearningservices.InferencePoolPropertiesArgs( node_sku_type="Standard_VM", code_configuration=machinelearningservices.CodeConfigurationArgs( scoring_script="score.py", ), environment_configuration=machinelearningservices.EnvironmentConfigurationArgs( environment_variables={"EXAMPLE_ENV": "example_value"}, ), request_configuration=machinelearningservices.RequestConfigurationArgs( max_concurrent_requests_per_instance=5, ), ), ) # Define the Inference Endpoint inference_endpoint = machinelearningservices.InferenceEndpoint("ai_inference_endpoint", resource_group_name=resource_group.name, workspace_name=workspace.name, location=workspace.location, inference_endpoint_properties=machinelearningservices.InferenceEndpointPropertiesArgs( inference_pool_name=inference_pool.name, ), ) # Export the endpoint URL for the inference services pulumi.export('endpoint_url', inference_endpoint.properties.apply(lambda props: props.endpoint_uri))

    This program defines an Azure Machine Learning workspace, an associated inference pool with auto-scaling capabilities, and an inference endpoint through which your machine learning model can be accessed. You will need to replace "score.py" with the actual path to your scoring script which is executed on each inference call, and ensure you have the necessary configurations set up (like environment variables or any other settings relevant to your machine learning model).

    The pulumi.export line at the end outputs the endpoint URL that you could use in your application to direct real-time inference requests. Once you run this Pulumi program with the appropriate Azure account setup, it will create the necessary infrastructure for your AI inference service.