1. Managing Access to AI Model Endpoints


    When managing access to AI model endpoints, it's essential to ensure that only authorized users and services can interact with your models. Whether you are deploying your models on Azure, Google Cloud, or AWS, each cloud provider offers a way to manage access using IAM (Identity and Access Management) policies, and Pulumi provides a way to define and assign these policies as code.

    For the purpose of this explanation, let's consider an example where we want to manage access to an AI model endpoint in Azure using the azure-native.machinelearningservices.ModelVersion resource. This resource represents a version of a machine learning model within Azure Machine Learning.

    Here is how you would define access controls for an Azure Machine Learning Services Model Version using Pulumi in Python:

    1. Declare the model version: This resource represents the specific model you want to manage access for.

    2. Configure identity and access management (IAM) policies: You need to define an IAM policy specifying who can access the model endpoint and what actions they can perform.

    3. Assign IAM roles to users or groups: The IAM policy you define will be associated with Azure AD (Active Directory) users, service principals, or groups that should have access to the model.

    Let's look at a basic example where we create an Azure Machine Learning workspace and a Model Version within it, and then manage access by setting an IAM policy. Remember to replace placeholder values with your actual resource names and details.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure Machine Learning workspace ml_workspace = azure_native.machinelearningservices.Workspace( "mlWorkspace", resource_group_name=pulumi.Config("resourceGroupName"), location=pulumi.Config("location"), sku=azure_native.machinelearningservices.SkuArgs( name="Standard" ), # Other properties as needed. ) # Create an Azure Machine Learning Model Version model_version = azure_native.machinelearningservices.ModelVersion( "modelVersion", name="myModel", version="1", workspace_name=ml_workspace.name, resource_group_name=ml_workspace.resource_group_name, model_version_properties=azure_native.machinelearningservices.ModelVersionPropertiesArgs( # Set properties for the Model Version. ) # Other properties as needed. ) # Manage IAM Role Assignment to Control Access to the model version role_assignment = azure_native.authorization.RoleAssignment( "roleAssignment", scope=model_version.id, # Assign the Role at the level of the Model Version role_definition_id="/subscriptions/{subscription-id}/providers/Microsoft.Authorization/roleDefinitions/{role-definition-id}", # The ID of the role definition principal_id="{principal-id}", # The ID of the principal (user, group, service principal, managed identity) that should receive the role assignment. ) # Export the endpoint of the Model Version pulumi.export('model_endpoint', model_version.endpoint_url)

    In this example, the mlWorkspace resource represents the Azure Machine Learning workspace, and the modelVersion refers to the specific version of your AI model. The role_assignment resource is used here to control who can access this model by assigning an appropriate IAM role to a principal (like a user or service principal).

    Remember, the role definition and principal IDs are unique identifiers that you will need to replace with the actual values from your Azure subscription and IAM setup. This will ensure that the correct permissions are applied to the correct entities.

    After deploying this Pulumi program, the specified principal would have the access determined by the assigned role to the AI Model Version.

    Always test IAM policies in a safe environment before applying them to production to ensure that they behave as expected and only grant the intended access.