1. Schema Definitions for AI Model APIs


    Creating an AI Model API involves defining the schema for the data the API will accept and provide. In the context of managing AI models with Pulumi, you would work with cloud services such as Azure's Machine Learning Services, or Google Cloud's AI Engine. These services allow you to create, update, and manage machine learning models and their versions at scale.

    In our Pulumi example, let's assume we want to create a new version of a machine learning model using Azure's Machine Learning Services. We will use the ModelVersion resource from the azure-native package to define the new version of our machine learning model within an Azure Machine Learning workspace.

    Below is a Pulumi program in Python that accomplishes this:

    import pulumi import pulumi_azure_native.machinelearningservices as ml # Create a new version of a model in Azure Machine Learning Services model_version = ml.ModelVersion( "modelVersion", # The name of the Machine Learning model name="myModelName", # The name of the version to create or update version="v1", # The name of the Azure Machine Learning workspace workspace_name="myWorkspace", # The resource group in which to create the model version resource_group_name="myResourceGroup", # Properties specific to the model version model_version_properties=ml.ModelVersionPropertiesArgs( tags={ "stage": "production", "type": "regression", }, # Additional model-specific properties description="This is a regression model for predicting values.", # If applicable, the URI where the model is located model_uri="azureml://registries/myRegistry/models/myModelName/versions/1" ) ) # Export the model version ID so it can be used in other places pulumi.export("model_version_id", model_version.id)

    In this program:

    1. We import the Pulumi SDKs for Python and specifically for Azure Machine Learning Services.
    2. We create a new ModelVersion resource, which defines a particular version of a machine learning model in our Azure Machine Learning workspace.
    3. Within the ModelVersion we specify properties such as the name of the model, the version identifier, and the workspace and resource group it belongs to.
    4. The model_version_properties allows us to add more detail to our model version, including tags for categorization, a description, and the model URI that points to the location of the model.
    5. Finally, we export the model version ID, which can be useful if we need to reference this model version from other parts of our infrastructure or applications.

    By defining your AI model schemas and versions in this way with Pulumi, you can version control your infrastructure definition, automate deployments, and integrate it into CI/CD pipelines, ensuring that your machine learning infrastructure evolves together with your applications and models.