1. Facilitating External API Calls for Machine Learning Models


    To facilitate external API calls for machine learning models, one would typically use cloud machine learning services that provide model deployment capabilities. For example, AWS SageMaker or Azure Machine Learning can host trained machine learning models and expose them via HTTP endpoints, which can be called from external services or applications.

    Assuming we want to deploy a machine learning model and expose it using an external API within Azure's cloud ecosystem, we would use the Azure Machine Learning services. With Pulumi, we can define the infrastructure needed to set up a model, register it within a workspace, and then deploy it to an Azure container instance or a Kubernetes cluster where it will be available as a web service.

    Below is a Python program using Pulumi that sets up a machine learning workspace, registers a model, and then deploys it to an Azure Container Instance. The program details and comments will guide you through the steps taken to accomplish this task:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import machinelearningservices # Set up a machine learning workspace # This workspace acts as a container for the machine learning activities and models. # For more information on workspaces, refer to: # https://www.pulumi.com/registry/packages/azure-native/api-docs/machinelearningservices/workspace/ ml_workspace = machinelearningservices.Workspace( "mlWorkspace", resource_group_name="my-rg", # Replace with your resource group name location="East US", # Replace with the desired location sku=azure_native.machinelearningservices.SkuArgs(name="Basic") ) # Register a machine learning model # The model registration associates the model binaries (e.g. TensorFlow or PyTorch model) with a name and version. # For model registration details, refer to: # https://www.pulumi.com/registry/packages/azure-native/api-docs/machinelearningservices/registrymodelversion/ ml_model = machinelearningservices.ModelContainer( "mlModel", resource_group_name="my-rg", # Replace with your resource group name name="myModel", workspace_name=ml_workspace.name, properties=machinelearningservices.ModelContainerType( description="My machine learning model", model_uri="azure://path/to/model", # Replace with the path to your model in Azure Blob Storage model_framework="TensorFlow", # Specify the framework of the model (TensorFlow, PyTorch, etc.) model_framework_version="2.0" # Specify the version of the framework ) ) # Deploy the model as a web service on an Azure Container Instance # This deployment creates a containerized REST endpoint for the model, making it accessible for external API calls. # Deployment details can be found at: # https://www.pulumi.com/registry/packages/azure-native/api-docs/machinelearningservices/compute/ ml_service_deployment = machinelearningservices.Compute( "mlServiceDeployment", resource_group_name="my-rg", # Replace with your resource group name compute_name="myDeployment", workspace_name=ml_workspace.name, properties=azure_native.machinelearningservices.ComputeEffect( compute_type="ACI", # Azure Container Instance resource_id=ml_model.id, location="East US" # Match the location with your ML workspace ) ) # Export the scoring URI of the deployed model # This is the URI that you would use to make HTTP POST requests with model input data for predictions. pulumi.export("scoring_uri", ml_service_deployment.properties.apply(lambda props: props.property_dict.get("scoringUri")))

    In this program:

    • We establish a new Azure Machine Learning workspace, within which all associated resources for machine learning activities will be contained.
    • We register the machine learning model to the workspace. This step involves providing information about the model, such as its location in Azure Blob Storage, the machine learning framework it uses (TensorFlow, in this case), and the version of the framework.
    • We deploy the registered model to an Azure Container Instance (ACI), which provides an isolated environment for running containers.
    • Finally, we export the scoring URI of the deployed model. This URI is used to interact with the deployed model through HTTP POST requests.

    Please ensure you replace "my-rg", "azure://path/to/model", and "myDeployment" with your actual resource group name, model storage URI, and desired deployment name. Also, ensure that you have the required permissions and the Azure Machine Learning workspace name chosen is unique within your subscription.

    After deploying this Pulumi program to your Azure environment, you should have a live machine learning model that can be accessed via an external API making HTTP POST requests with input data for predictions.