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-[Visual Studio Code with the Azure Machine Learning extension](how-to-setup-vs-code.md)
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## Prerequisites
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- An Azure subscription with a free or paid version of Azure Machine Learning. If you don't have an Azure subscription, [create a free account before you begin](https://azure.microsoft.com/free/).
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-If you want to run the Azure CLI commands in this article locally, you need [Azure CLI](/cli/azure/install-azure-cli) installed.
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-[Azure CLI](/cli/azure/install-azure-cli) installed, if you want to run the Azure CLI commands in this article locally.
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If you run the Azure CLI commands in [Azure Cloud Shell](https://azure.microsoft.com//features/cloud-shell/), you don't need to install anything. The browser accesses the latest cloud version of Azure CLI and the Azure Machine Learning extension.
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@@ -42,6 +42,10 @@ You can also use the following methods to create and manage Azure Machine Learni
- The Azure Machine Learning workspace uses Azure Container Registry for some operations, and automatically creates a Container Registry instance when it first needs one.
If you use Azure Cloud Shell from the Azure portal, you can skip this section. The cloud shell automatically authenticates you using the Azure subscription you're signed in with.
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After you create the workspace by running `az ml workspace create -g <resource-group-name> --file <configuration-file>.yml`, use the [Azure networking CLI commands](/cli/azure/network/private-endpoint#az-network-private-endpoint-create) to create a private link endpoint for the workspace.
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After you create the workspace by running `az ml workspace create`, use the [Azure networking CLI commands](/cli/azure/network/private-endpoint#az-network-private-endpoint-create) to create a private link endpoint for the workspace.
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```azurecli-interactive
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az network private-endpoint create \
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--zone-name 'privatelink.notebooks.azure.net'
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```
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For more information on using a private endpoint and virtual network with your workspace, see:
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For more information on using a private endpoint and virtual network with your workspace, see the following articles:
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-[Configure a private endpoint for your Azure Machine Learning workspace](how-to-configure-private-link.md).
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-[Private endpoint configuration for your Azure Machine Learning workspace](how-to-configure-private-link.md)
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-[Virtual network isolation and privacy overview](how-to-network-security-overview.md)
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### Resource management private links
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Moving an Azure Machine Learning workspace is currently in preview. For more information, see [Move Azure Machine Learning workspaces between subscriptions (preview)](how-to-move-workspace.md).
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### Delete the Azure container registry
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The Azure Machine Learning workspace uses Azure Container Registry for some operations, and automatically creates a Container Registry instance when it first needs one.
To delete a workspace after it's no longer needed, use the following command:
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az ml workspace delete -n <workspace-name> -g <resource-group-name>
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```
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The default behavior for Azure Machine Learning is to *softdelete* the workspace. The workspace isn't immediately deleted, but instead is marked for deletion. For more information, see [Soft delete](./concept-soft-delete.md).
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The default behavior for Azure Machine Learning is to *soft-delete* the workspace. The workspace isn't immediately deleted, but instead is marked for deletion. For more information, see [Soft delete](./concept-soft-delete.md).
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