1. API Frontend for Machine Learning Model Deployment


    When deploying a machine learning model as an API frontend, you'll typically need to manage a few key resources:

    1. A model registry where you store the trained machine learning models.
    2. A compute resource capable of running the model inference code.
    3. An endpoint where users can send data to the model and receive predictions.

    Assuming you want to deploy on Azure, we can use a combination of Azure Machine Learning workspace and compute instances to manage the model and run the inference code, along with an inference endpoint for users to interact with the model.

    Here is the program in Python using Pulumi that sets up such an infrastructure:

    1. azure_native.machinelearningservices.Workspace is used to create an Azure Machine Learning workspace, which is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.
    2. azure_native.machinelearningservices.Compute represents a compute resource, such as a dedicated VM or a cluster, where you can run machine learning workloads.
    3. azure_native.machinelearningservices.InferenceEndpoint is used to deploy the trained model as a web service to receive data, perform predictions, and return the results.

    Let's write a Pulumi program that sets up this architecture:

    import pulumi import pulumi_azure_native.machinelearningservices as ml_services # Create an Azure Machine Learning Workspace ml_workspace = ml_services.Workspace("mlWorkspace", location="East US", resource_group_name="my-ml-resource-group", sku=ml_services.SkuArgs( name="Standard", tier="Enterprise", ), description="My Machine Learning Workspace" ) # Create a compute instance within the workspace to run our model ml_compute = ml_services.Compute("mlCompute", location="East US", resource_group_name="my-ml-resource-group", compute_name="myComputeInstance", properties=ml_services.ComputeInstanceArgs( compute_type="AmlCompute", properties=ml_services.AmlComputePropertiesArgs( vm_size="STANDARD_D2_V2", vm_priority="Dedicated" ), scale_settings=ml_services.ScaleSettingsArgs( auto_scale=ml_services.AutoScaleArgs( min_node_count=1, max_node_count=4 ) ) ) ) # Create an inference endpoint to serve predictions from the deployed model inference_endpoint = ml_services.InferenceEndpoint("mlInferenceEndpoint", location="East US", resource_group_name="my-ml-resource-group", endpoint_name="myModelEndpoint", properties=ml_services.EndpointVariantArgs( description="Endpoint to serve machine learning model", type="RealTime", auth_mode="Key" ) ) # Export the HTTP endpoint to access the deployed model pulumi.export("inference_http_endpoint", inference_endpoint.properties.apply(lambda props: props.scoring_uri))

    This program will create the necessary resources for deploying a machine learning model as an API frontend in Azure:

    • First, it sets up a machine learning workspace named mlWorkspace that acts as a container for the ML assets and computes resources.
    • Then, it creates a compute instance mlCompute for running the inference code. This instance is set to auto-scale based on demand with a minimum of 1 node and a maximum of 4 nodes.
    • An inference endpoint mlInferenceEndpoint is then configured to host the deployed model and receive prediction requests.
    • Finally, the scoring URI of the inference endpoint is exported. This is the URL that you can hit with data to get predictions.

    After running this Pulumi program, you would need to handle model training and deployment separately, perhaps using the Azure Machine Learning SDK in Python. That involves creating and registering a model, defining a scoring script to process input data, and deploying the model to the compute instance.

    The scoring script typically receives input data in a JSON format, runs the model's predict function on that data, and then returns the model's predictions.

    Make sure you have the Azure CLI or Pulumi Azure provider properly configured with the necessary credentials to create these resources on Azure. After deploying this stack, you can interact with the model by sending POST HTTP requests to the endpoint's URI with input data for predictions.