![]() ![]() Starts encrypting the local scratch disk for Azure Machine Learning compute clusters, providing you have not created any previous clusters in your subscription. For more information, see Encryption at rest in Azure Cosmos DB.Īn additional configuration you can provide for your data is to set the confidential_data parameter to true. When using a customer-managed key, Azure Machine Learning creates a secondary resource group which contains the Azure Cosmos DB instance. ![]() Customer-managed keys are used to create a new Azure Cosmos DB instance for the workspace.Ĭmk_keyvault="/subscriptions//resourceGroups//providers/Microsoft.KeyVault/vaults/" ` Uses an existing Azure Key Vault to retrieve customer-managed keys.This creates a new Azure Cosmos DB instance. Enable high confidentiality settings for the workspace.The following example template demonstrates how to create a workspace with three settings: storageAccountName "existingstorageaccountname" StorageAccountName="existingstorageaccountname" parameters workspaceName="exampleworkspace" \ For example, if you want to use an existing storage account set the storageAccountOption value to existing and provide the name of your storage account in the storageAccountName parameter. By providing additional parameters to the template, you can use existing resources. However, you also have the option of using existing resources. parameters workspaceName="exampleworkspace" location="eastus"īy default, all of the resources created as part of the template are new. The workspaceName, which is the friendly name of the Azure Machine Learning workspace. If you select a location where it is not available, the service will be created in the South Central US location. The exception is the Application Insights service, which is not available in all of the locations that the other services are. The template will use the location you select for most resources. The location where the resources will be created. The example template has two required parameters: The various services are required by the Azure Machine Learning workspace. The resource group is the container that holds the services. ![]() This template creates the following Azure services: The Azure Resource Manager template used throughout this document can be found in the microsoft.machineleaerningservices/machine-learning-workspace-vnet directory of the Azure quickstart templates GitHub repository. If you want to create a template that deploys multiple workspaces in the same VNet, set this up manually (using the Azure Portal or CLI) and then use the Azure portal to generate a template. This is because the template creates new DNS zones during deployment. The template doesn't support multiple Azure Machine Learning workspaces deployed in the same VNet. The following example is an entry for Azure Machine Learning: "type": "Microsoft.MachineLearningServices/workspaces", To update the API version, find the "apiVersion": "YYYY-MM-DD" entry for the resource type and update it to the latest version. For information on the API for a specific service, check the service information in the Azure REST API reference. For information on how to see if it is registered and how to register it, see the Azure resource providers and types article.Įach Azure service has its own set of API versions. ![]() The resource provider for Azure Machine Learning is Microsoft.MachineLearningServices. For example, creating a workspace in subscription A that uses a storage account from subscription B, the Azure Machine Learning namespace must be registered in subscription B before you can use the storage account with the workspace. If you want to use existing services from a different Azure subscription than the workspace, you must register the Azure Machine Learning namespace in the subscription that contains those services. When creating a new workspace, you can either automatically create services needed by the workspace or use existing services. To use a template from a CLI, you need either Azure PowerShell or the Azure CLI. If you do not have one, try the free or paid version of Azure Machine Learning. Parameters are used to provide input values when using the template.įor more information, see Deploy an application with Azure Resource Manager template. It may also specify deployment parameters. A template is a JSON document that defines the resources that are needed for a deployment. A Resource Manager template makes it easy to create resources as a single, coordinated operation. In this article, you learn several ways to create an Azure Machine Learning workspace using Azure Resource Manager templates. ![]()
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