1. Kubernetes Azure Identity Binding for AI Data Access


    We are going to create a Kubernetes on Azure that includes an Azure Identity Binding that can be used to access AI data services such as Azure Machine Learning. The identity binding mechanism provides a secure way for your Kubernetes services to interact with Azure resources without having to hardcode credentials.

    Here's a breakdown of what we'll do in our Pulumi Python program:

    1. Create an Azure Kubernetes Service (AKS) cluster using the azure-native.containerservice.ManagedCluster class.
    2. Set up a Managed Identity using azure-native.managedidentity.UserAssignedIdentity.
    3. Bind the Managed Identity to the AKS cluster so our applications running on Kubernetes can access Azure services, in this case, to perform tasks related to AI data management. This can be done using the azure_native.containerservice.TrustedAccessRoleBinding resource, although in our snippet we will simulate this step because exact implementation details are abstracted.
    4. For completeness, we'll also touch upon how one would provision resources pertaining to Azure Machine Learning, but we will not dive deeply into Azure Machine Learning specific resources.

    Let's start by setting up the AKS cluster and the user-assigned identity:

    import pulumi from pulumi_azure_native import resources, containerservice, managedidentity # Create a resource group for our resources resource_group = resources.ResourceGroup("resource_group") # Create an AKS cluster aks_cluster = containerservice.ManagedCluster( "aksCluster", resource_group_name=resource_group.name, agent_pool_profiles=[{ "count": 3, "max_pods": 110, "mode": "System", "name": "agentpool", "node_labels": {}, "os_disk_size_gb": 30, "os_type": "Linux", "vm_size": "Standard_DS2_v2", "vnet_subnet_id": "<your-vnet-subnet-id>", # Replace with your VNet Subnet ID }], dns_prefix="aksk8s", enable_rbac=True, # Enable RBAC for secure access ) # Create a user-assigned managed identity for AKS cluster to access other Azure services identity = managedidentity.UserAssignedIdentity( "aksIdentity", resource_group_name=resource_group.name, ) # The following is a representation of granting the identity permissions to access Azure Machine Learning, or similar services. # The specifics of the role binding will depend on the exact requirements and permissions. # Here we're assuming that there's a role with appropriate permissions to access AI data services. # Typically, the role would be created and then assigned to the managed identity. # role_assignment = azure.authorization.RoleAssignment( # 'roleAssignment', # scope=pulumi.Output.concat('/subscriptions/', pulumi_azure.config.subscription_id, '/resourceGroups/', resource_group.name), # role_definition_name='Contributor', # The role with necessary permissions, might need to be more specific for AI services # principal_id=identity.principal_id, # ) # Export the Kubernetes cluster name and the managed identity ID pulumi.export("cluster_name", aks_cluster.name) pulumi.export("identity_id", identity.id)

    In the code above, firstly we create a resource group which is a container that holds related resources for an Azure solution.

    Then, we proceed to create an Azure Kubernetes Service (AKS) cluster within this resource group. This AKS cluster is where your Kubernetes applications will run.

    We also create a User Assigned Identity that is independent of the AKS cluster lifecycle. This identity will allow you to grant permissions to your Kubernetes workloads to access other Azure resources without the need to manage credentials.

    A critical note is that vnet_subnet_id should be replaced with your own VNet Subnet ID where your AKS should be provisioned, and naturally, in a real-world scenario, you should use real role definitions and scopes appropriate for your use case.

    The role assignment commented out would typically be another piece of code to run, but since exact resource types and operations for role assignments vary greatly depending on what exactly is needed from the AI or Machine Learning service being accessed (like specific data read/write operations), we're representing it as a high-level concept. Refer to Azure Role-Based Access Control for more details on how to configure role assignments.

    Once the setup is complete, your applications running on AKS will utilize this identity to access Azure AI services securely, assuming proper role-based access control permissions are in place.

    Lastly, we export the cluster name and the identity ID so you can easily reference them, for example, when setting up CI/CD pipelines or in other parts of your infrastructure.

    Remember to replace placeholder values with actual values that match your scenario. This program assumes you have already set up the Pulumi Azure provider, and it should be run in an environment where the Pulumi CLI has been authenticated to your Azure subscription.