1. Private Endpoint Name Resolution for Azure Machine Learning Services


    To set up Private Endpoint Name Resolution for Azure Machine Learning Services using Pulumi, you will need to define various Azure resources that together enable a secure and private connection to the Azure Machine Learning workspace. This typically involves creating a virtual network, a private DNS zone, a private endpoint, and configuring the necessary networking and DNS settings to ensure that the Azure Machine Learning workspace is accessible only through the private endpoint.

    Here’s a Pulumi program that demonstrates how to create these resources using the azure-native provider in Python:

    import pulumi import pulumi_azure_native.network as network from pulumi_azure_native.machinelearningservices import Workspace # Create a new resource group resource_group = network.ResourceGroup('rg', resource_group_name='ml-private-endpoint-rg') # Create a virtual network vnet = network.VirtualNetwork('vnet', resource_group_name=resource_group.name, address_space=network.AddressSpaceArgs( address_prefixes=['']), location=resource_group.location) # Create a subnet for the Azure Machine Learning Service, with the necessary delegation. subnet = network.Subnet('ml-subnet', resource_group_name=resource_group.name, virtual_network_name=vnet.name, address_prefix='', delegations=[network.DelegationArgs( name='ml-delegation', service_delegation=network.ServiceDelegationArgs( name='Microsoft.MachineLearningServices/workspaces', ) )]) # Create a Private DNS Zone for the Azure Machine Learning workspace dns_zone = network.PrivateDnsZone('private-dns-zone', resource_group_name=resource_group.name, location='global', private_zone_name='privatelink.azureml.ms') # Link the DNS zone to the virtual network dns_vnet_link = network.VirtualNetworkLink('dns-vnet-link', resource_group_name=resource_group.name, virtual_network_link_name='ml-vnet-dns-link', private_dns_zone_name=dns_zone.name, virtual_network_id=vnet.id, registration_enabled=True) # Create an Azure Machine Learning workspace ml_workspace = Workspace('ml-workspace', resource_group_name=resource_group.name, location=resource_group.location, workspace_name='my-ml-workspace', sku='Basic') # Create a Private Endpoint for the workspace private_endpoint = network.PrivateEndpoint('ml-private-endpoint', resource_group_name=resource_group.name, location=resource_group.location, private_endpoint_name='my-ml-private-endpoint', subnet=subnet.id, private_link_service_connections=[network.PrivateLinkServiceConnectionArgs( name='ml-private-connection', private_link_service_id=ml_workspace.id, group_ids=['workspace'], )]) # Create a DNS zone group for the Private Endpoint dns_zone_group = network.PrivateDnsZoneGroup('dns-zone-group', resource_group_name=resource_group.name, private_dns_zone_group_name='ml-workspace-dns-zone-group', private_dns_zone_configs=[ network.PrivateDnsZoneConfigArgs( private_dns_zone_id=dns_zone.id, name='ml-dns-zone-config' ) ], private_endpoint_name=private_endpoint.name) # Export the URL of the Azure Machine Learning workspace pulumi.export('workspace_url', ml_workspace.discovery_url) # Export the Private Endpoint's connection status pulumi.export('private_endpoint_connection_status', private_endpoint.private_service_connection[0].status)

    In this program:

    1. We create a new Resource Group rg which is a container that holds related resources for an Azure solution.
    2. We define a Virtual Network vnet where Azure resources will be securely connected to each other.
    3. We then create a subnet ml-subnet within the virtual network, with delegation to the Azure Machine Learning workspace that allows an Azure Machine Learning Service to be injected into the subnet.
    4. Next, we set up a Private DNS Zone private-dns-zone for the Azure Machine Learning workspace.
    5. We link the DNS zone to the virtual network through dns-vnet-link to ensure that the DNS resolves within our virtual network.
    6. Then, we establish an Azure Machine Learning workspace ml-workspace which is where all the machine learning activities - from data preparation to model training and deployment - occur.
    7. We create a Private Endpoint ml-private-endpoint and connect it to the Azure Machine Learning workspace. A Private Endpoint uses a private IP address from the Virtual Network to effectively bring the service into the Virtual Network.
    8. Finally, we create a DNS zone group dns-zone-group which is used to manage and resolve the DNS of the Private Endpoint within the Virtual Network.

    The output includes the discovery URL of the workspace and the private endpoint connection status, allowing you to validate the setup.

    Remember to replace placeholders like resource_group_name and workspace_name with your own values. Also, you need to have the correct permissions and the necessary Azure credentials configured to deploy this stack.