edits

v-thepet · v-thepet · commit 991c394f8e45 · 2024-06-15T10:55:01.000-07:00
diff --git a/articles/machine-learning/how-to-manage-workspace-terraform.md b/articles/machine-learning/how-to-manage-workspace-terraform.md
@@ -1,30 +1,30 @@
 ---
-title: Manage workspaces by using Terraform
+title: Create workspaces by using Terraform
 titleSuffix: Azure Machine Learning
-description: Learn how to create and manage Azure Machine Learning workspaces by using Terraform.
+description: Learn how to create Azure Machine Learning workspaces with public or private connectivity by using Terraform.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: enterprise-readiness
 ms.custom: devx-track-terraform
 ms.author: deeikele
 author: denniseik
 ms.reviewer: larryfr
-ms.date: 06/14/2024
+ms.date: 06/17/2024
 ms.topic: how-to
 ms.tool: terraform
 ---
 
 # Manage Azure Machine Learning workspaces by using Terraform
 
-In this article, you learn how to create and manage an Azure Machine Learning workspace using Terraform configuration files. [Terraform](/azure/developer/terraform/) template-based configuration files enable you to define, create, and configure Azure resources in a repeatable and predictable manner. Terraform tracks resource state and can clean up and destroy resources.
+In this article, you learn how to create an Azure Machine Learning workspace by using Terraform configuration files. [Terraform](/azure/developer/terraform/) template-based configuration files enable you to define, create, and configure Azure resources in a repeatable and predictable manner. Terraform tracks resource state and can clean up and destroy resources.
 
-A Terraform configuration is a document that defines the resources needed for a deployment. The configuration can also specify deployment variables you can use to provide input values when you use the configuration.
+A Terraform configuration file is a document that defines the resources needed for a deployment. The Terraform configuration can also specify deployment variables to use to provide input values when you apply the configuration.
 
 ## Prerequisites
 
 - 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/).
-- [Azure CLI](/cli/azure/install-azure-cli) installed.
-- Terraform configured according to the instructions in this article and in [Configure Terraform in Azure Cloud Shell with Bash](/azure/developer/terraform/get-started-cloud-shell).
+- Terraform installed and configured according to the instructions in [Quickstart: Install and configure Terraform](/azure/developer/terraform/quickstart-configure).
+<!--- [Azure CLI](/cli/azure/install-azure-cli) installed.-->
 
 ## Limitations
 
@@ -34,30 +34,22 @@ A Terraform configuration is a document that defines the resources needed for a
 
   [!INCLUDE [application-insight](includes/machine-learning-application-insight.md)]
 
-## Declare the Azure provider
+## Create the workspace
 
-Create the Terraform configuration file that declares the Azure provider:
+First, declare the Azure provider in a file named *main.tf* that has the following code.
 
-1. In a Bash shell, create a new file named *main.tf*.
+:::code language="terraform" source="~/terraform/quickstart/101-machine-learning/main.tf":::
 
-   ```bash
-   code main.tf
-   ```
+### Configure the workspace
 
-1. Paste the following code into the editor:
+To create an Azure Machine Learning workspace, use one of the following Terraform configurations. An Azure Machine Learning workspace requires various other services as dependencies. The template specifies these [associated resources](./concept-workspace.md#associated-resources). Depending on your needs, you can choose to use a template that creates resources with either public or private network connectivity.
 
-   :::code language="terraform" source="~/terraform/quickstart/101-machine-learning/main.tf":::
-
-1. Save the file by pressing Ctrl+S and exit the editor by pressing Ctrl+Q.
-
-## Deploy a workspace
-
-Use one of the following Terraform configurations to create an Azure Machine Learning workspace. An Azure Machine Learning workspace requires various other services as dependencies. The template also specifies these [dependent associated resources](./concept-workspace.md#associated-resources). Depending on your needs, you can choose to use a template that creates resources with either public or private network connectivity.
+> [!NOTE]
+> Some resources in Azure require globally unique names. Before deploying your resources, make sure to set `name` variables to unique values.
 
 # [Public network](#tab/publicworkspace)
 
-The following configuration crates a workspace with public network connectivity.
-<!-- Some resources in Azure require globally unique names. Before deploying your resources using the following templates, set the `name` variable to a value that is unique.-->
+The following configuration creates a workspace with public network connectivity.
 
 Define the following variables in a file called *variables.tf*.
 
@@ -69,10 +61,9 @@ Define the following workspace configuration in a file called *workspace.tf*:
 
 # [Private network](#tab/privateworkspace)
 
-The following configuration creates a workspace in an isolated network environment by using Azure Private Link endpoints. The template includes [private DNS zones](../dns/private-dns-privatednszone.md) to resolve domain names within the virtual network.
-<!-- Some resources in Azure require globally unique names. Before deploying your resources using the following templates, set the `resourceprefix` variable to a value that is unique.-->
+The following configuration creates a workspace in an isolated network environment by using Azure Private Link endpoints. The template includes [private Domain Name System (DNS) zones](../dns/private-dns-privatednszone.md) to resolve domain names within the virtual network.
 
-If you use private link endpoints for both Azure Container Registry and Azure Machine Learning, you can't use Container Registry tasks for building [environment](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) images. Instead you can build images by using an Azure Machine Learning compute cluster.
+If you use private link endpoints for both Azure Container Registry and Azure Machine Learning, you can't use Container Registry tasks for building [environment](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) images. Instead you must build images by using an Azure Machine Learning compute cluster.
 
 To configure the cluster name to use, set the [image_build_compute_name](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument. You can also [allow public access](./how-to-configure-private-link.md?tabs=python#enable-public-access) to a workspace that has a private link endpoint by using the [public_network_access_enabled](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument.
 
@@ -87,7 +78,7 @@ Define the following workspace configuration in a file called *workspace.tf*:
 Define the following network configuration in a file called *network.tf*:
 
 ```terraform
-# Virtual Network
+# Virtual network
 resource "azurerm_virtual_network" "default" {
   name                = "vnet-${var.name}-${var.environment}"
   address_space       = var.vnet_address_space
@@ -118,30 +109,44 @@ resource "azurerm_subnet" "snet-workspace" {
   address_prefixes                               = var.ml_subnet_address_space
   enforce_private_link_endpoint_network_policies = true
 }
-
-# ...
-# For a full reference, see: https://github.com/Azure/terraform/blob/master/quickstart/201-machine-learning-moderately-secure/network.tf
 ```
 
-To learn more about the options to connect to your private link endpoint workspace, see [Securely connect to your workspace](./how-to-secure-workspace-vnet.md#securely-connect-to-your-workspace).
+- For a full reference, see [201: Machine learning workspace, compute, and a set of network components for network isolation](https://github.com/Azure/terraform/blob/master/quickstart/201-machine-learning-moderately-secure/network.tf).
+- To learn more about how to connect to your private link endpoint workspace, see [Securely connect to your workspace](./how-to-secure-workspace-vnet.md#securely-connect-to-your-workspace).
 
 ---
 
+## Create and apply the plan
+
+To create the workspace, run the following code:
+
+```terraform
+terraform init
+
+terraform plan \
+          -var name=<deployment-name> \
+          -var environment=<environment-name> \
+        # -var <any of the variables set in variables.tf> \
+          -out demo.tfplan
+
+terraform apply "demo.tfplan"
+```
+
 ## Troubleshoot resource provider errors
 
 [!INCLUDE [machine-learning-resource-provider](includes/machine-learning-resource-provider.md)]
 
 ## Related resources
 
 - To learn more about Terraform support on Azure, see [Terraform on Azure documentation](/azure/developer/terraform/).
-- For details on the Terraform Azure provider and Machine Learning module, see [Terraform Registry Azure Resource Manager Provider](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace).
-- To find quickstart template examples for Terraform, see [Azure Terraform QuickStart Templates](https://github.com/Azure/terraform/tree/master/quickstart).
+- For details on the Terraform Azure provider and Machine Learning module, see [Terraform Registry Azure Resource Manager provider](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace).
+- To find quickstart template examples for Terraform, see the following [Azure Terraform quickstart templates](https://github.com/Azure/terraform/tree/master/quickstart).
   
-  - [101: Machine learning workspace and compute](https://github.com/Azure/terraform/tree/master/quickstart/101-machine-learning) – the minimal set of resources needed to get started with Azure Machine Learning.
-  - [201: Machine learning workspace, compute, and a set of network components for network isolation](https://github.com/Azure/terraform/tree/master/quickstart/201-machine-learning-moderately-secure) – all resources that are needed to create a production-pilot environment for use with HBI data.
-  - [202: Similar to 201, but with the option to bring existing network components.](https://github.com/Azure/terraform/tree/master/quickstart/202-machine-learning-moderately-secure-existing-VNet).
-  - [301:  Machine Learning workspace (Secure Hub and Spoke with Firewall)](https://github.com/azure/terraform/tree/master/quickstart/301-machine-learning-hub-spoke-secure).
+  - [101: Machine learning workspace and compute](https://github.com/Azure/terraform/tree/master/quickstart/101-machine-learning) provides the minimal set of resources needed to get started with Azure Machine Learning.
+  - [201: Machine learning workspace, compute, and a set of network components for network isolation](https://github.com/Azure/terraform/tree/master/quickstart/201-machine-learning-moderately-secure) provides all resources needed to create a production-pilot environment for use with high business impact (HBI) data.
+  - [202: Similar to 201, but with the option to bring existing network components](https://github.com/Azure/terraform/tree/master/quickstart/202-machine-learning-moderately-secure-existing-VNet).
+  - [301: Machine Learning workspace (secure hub and spoke with firewall)](https://github.com/azure/terraform/tree/master/quickstart/301-machine-learning-hub-spoke-secure).
   
 - To learn more about network configuration options, see [Secure Azure Machine Learning workspace resources using virtual networks](./how-to-network-security-overview.md).
 - For alternative Azure Resource Manager template-based deployments, see [Deploy resources with Resource Manager templates and Resource Manager REST API](/azure/azure-resource-manager/templates/deploy-rest).
-- For information on how to keep your Azure Machine Learning up to date with the latest security updates, see [Vulnerability management](concept-vulnerability-management.md).
+- For information on how to keep your Azure Machine Learning workspace up to date with the latest security updates, see [Vulnerability management](concept-vulnerability-management.md).
