1. Hosting AI Model Serving Infrastructure Code.


    To host AI model serving infrastructure, you would typically need a cloud service that provides machine learning model hosting capabilities. The choice of service would depend on the cloud provider you're interested in. For instance, Google Cloud offers AI Platform Predictions for serving machine learning models, while Azure Machine Learning service can be used to deploy and serve models on Azure.

    Below is a Pulumi Python program that demonstrates how to deploy a machine learning model serving infrastructure on Azure. This program uses Azure Machine Learning services to create a model, a workspace, and an endpoint for serving predictions.

    Make sure you have an Azure account and have configured your Pulumi CLI with the correct credentials.

    Program Explanation

    1. We are using resources from pulumi_azure_native.machinelearningservices to define various components necessary for serving a machine learning model.

    2. We start with creating a resource group, which is a container that holds related resources for an Azure solution.

    3. Then, we define an Azure Machine Learning workspace. A workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.

    4. We continue by creating a model container which logically groups models. This specific resource is not necessary for serving a model but organizes models better and is considered a best practice.

    5. Finally, we create a model version which registers the model in the container, and specify its attributes such as the model's URL, type, and any properties. The modelUri is expected to be a path to the serialized model which can be in Azure Blob storage or any accessible location.

    6. After creating these resources, your machine learning model would be registered and ready to be deployed as a web service for prediction purposes.

    Here's how the code for setting up the described infrastructure would look:

    import pulumi from pulumi_azure_native import machinelearningservices as ml from pulumi_azure_native import resources # Create an Azure resource group resource_group = resources.ResourceGroup("ai_model_resource_group") # Create an Azure Machine Learning workspace workspace = ml.Workspace("ai_model_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=ml.SkuArgs(name="Basic"), ) # Define a container for the machine learning models model_container = ml.RegistryModelContainer("ai_model_container", resource_group_name=resource_group.name, workspace_name=workspace.name, ) # Register a model version model_version = ml.RegistryModelVersion("ai_model_version", resource_group_name=resource_group.name, workspace_name=workspace.name, model_container_name=model_container.name, model_version_properties=ml.ModelVersionType( model_uri="https://<your_storage_account>.blob.core.windows.net/<your_container>/<your_model_path>", # Replace with your model's URI model_type="MLFlowModel", # Replace this with your model's actual type description="A machine learning model for serving.", tags={"stage": "production"}, ), ) # Export the model's registration details pulumi.export("model_name", model_version.name) pulumi.export("model_version", model_version.version)

    In the model_version resource, the model_uri should point to your machine-learning model's location, which is accessible to Azure services; this could be an Azure Blob Storage URL where your serialized model is stored.

    Please replace <your_storage_account>, <your_container>, and <your_model_path> with the actual storage account name, container name, and model path you have your model in.

    After running this program with Pulumi, your model will be registered and ready for the next steps, which may involve creating an endpoint for serving predictions using your model. Those steps vary depending on the model type and deployment configuration, and they may require additional resources like compute instances or clusters.