Merge pull request #176918 from cmcclister/cm-linkfix-20211021-6

Links: azure-docs-pr (2021-10) - 6

Author: cmcclister

articles/machine-learning/how-to-deploy-managed-online-endpoints.md

@@ -207,7 +207,7 @@ This deployment might take up to 15 minutes, depending on whether the underlying
 > [!TIP]
 > * If you prefer not to block your CLI console, you may add the flag `--no-wait` to the command. However, this will stop the interactive display of the deployment status.
 >
-> * Use [Troubleshooting managed online endpoints deployment (preview)](how-to-troubleshoot-managed-online-endpoints.md) to debug errors.
+> * Use [Troubleshooting managed online endpoints deployment (preview)](./how-to-troubleshoot-online-endpoints.md) to debug errors.
 
 ### Check the status of the deployment
 
@@ -330,4 +330,4 @@ To learn more, review these articles:
 - [Use batch endpoints (preview) for batch scoring](how-to-use-batch-endpoint.md)
 - [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md)
 - [Tutorial: Access Azure resources by using a managed online endpoint and system-managed identity (preview)](tutorial-deploy-managed-endpoints-using-system-managed-identity.md)
-- [Troubleshoot managed online endpoints deployment](how-to-troubleshoot-managed-online-endpoints.md)
+- [Troubleshoot managed online endpoints deployment](./how-to-troubleshoot-online-endpoints.md)

articles/machine-learning/how-to-deploy-with-rest.md

@@ -153,10 +153,10 @@ If you aren't going use the deployment, you should delete it with the below comm
 
 * Learn how to deploy your model [using the Azure CLI](how-to-deploy-managed-online-endpoints.md).
 * Learn how to deploy your model [using studio](how-to-use-managed-online-endpoint-studio.md).
-* Learn to [Troubleshoot managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
+* Learn to [Troubleshoot managed online endpoints deployment and scoring (preview)](./how-to-troubleshoot-online-endpoints.md)
 * Learn how to [Access Azure resources with a managed online endpoint and system-managed identity (preview)](tutorial-deploy-managed-endpoints-using-system-managed-identity.md)
 * Learn how to [monitor online endpoints](how-to-monitor-online-endpoints.md).
 * Learn [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md).
 * [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md).
 * [Managed online endpoints SKU list (preview)](reference-managed-online-endpoints-vm-sku-list.md).
-* Learn about limits on managed online endpoints in [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview).
+* Learn about limits on managed online endpoints in [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview).

articles/machine-learning/how-to-manage-quotas.md

@@ -197,4 +197,4 @@ When you're requesting a quota increase, select the service that you have in min
 
 + [Plan and manage costs for Azure Machine Learning](concept-plan-manage-cost.md)
 + [Service limits in Azure Machine Learning](resource-limits-quotas-capacity.md)
-+ [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
++ [Troubleshooting managed online endpoints deployment and scoring (preview)](./how-to-troubleshoot-online-endpoints.md)

articles/machine-learning/how-to-manage-workspace-terraform.md

@@ -46,7 +46,7 @@ Create the Terraform configuration file that declares the Azure provider:
 
 ## Deploy a workspace
 
-The following Terraform configurations can be used to create an Azure Machine Learning workspace. When you create an Azure Machine Learning workspace, various other services are required as dependencies. The template also specifies these [associated resources to the workspace](/azure/machine-learning/concept-workspace#resources). Depending on your needs, you can choose to use the template that creates resources with either public or private network connectivity.
+The following Terraform configurations can be used to create an Azure Machine Learning workspace. When you create an Azure Machine Learning workspace, various other services are required as dependencies. The template also specifies these [associated resources to the workspace](./concept-workspace.md#resources). Depending on your needs, you can choose to use the template that creates resources with either public or private network connectivity.
 
 # [Public network connectivity](#tab/publicworkspace)
 
@@ -60,11 +60,11 @@ Some resources in Azure require globally unique names. Before deploying your res
 
 # [Private network connectivity](#tab/privateworkspace)
 
-The configuration below creates a workspace in an isolated network environment using Azure Private Link endpoints. [Private DNS zones](/azure/dns/private-dns-privatednszone) are included so domain names can be resolved within the virtual network.
+The configuration below creates a workspace in an isolated network environment using Azure Private Link endpoints. [Private DNS zones](../dns/private-dns-privatednszone.md) are included so domain names can be resolved 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.
 
-When using private link endpoints for both Azure Container Registry and Azure Machine Learning, Azure Container Registry tasks cannot be used for building [environment](/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py&preserve-view=true) images. Instead you can build images using an Azure Machine Learning compute cluster. To configure the cluster name of use, set the [image_build_compute_name](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument. You can configure to [allow public access](/azure/machine-learning/how-to-configure-private-link?tabs=python#enable-public-access) to a workspace that has a private link endpoint using the [public_network_access_enabled](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument.
+When using private link endpoints for both Azure Container Registry and Azure Machine Learning, Azure Container Registry tasks cannot be used for building [environment](/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py&preserve-view=true) images. Instead you can build images using an Azure Machine Learning compute cluster. To configure the cluster name of use, set the [image_build_compute_name](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument. You can configure to [allow public access](./how-to-configure-private-link.md?tabs=python#enable-public-access) to a workspace that has a private link endpoint using the [public_network_access_enabled](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument.
 
 **variables.tf**:
 :::code language="terraform" source="~/terraform/quickstart/201-machine-learning-moderately-secure/variables.tf":::
@@ -110,7 +110,7 @@ resource "azurerm_subnet" "snet-workspace" {
 # For full reference, see: https://github.com/Azure/terraform/blob/master/quickstart/201-machine-learning-moderately-secure/network.tf
 ```
 
-There are several options to connect to your private link endpoint workspace. To learn more about these options, refer to [Securely connect to your workspace](/azure/machine-learning/how-to-secure-workspace-vnet#securely-connect-to-your-workspace).
+There are several options to connect to your private link endpoint workspace. To learn more about these options, refer to [Securely connect to your workspace](./how-to-secure-workspace-vnet.md#securely-connect-to-your-workspace).
 
 ---
 
@@ -134,5 +134,5 @@ There are several options to connect to your private link endpoint workspace. To
   * [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).
 
 * For details on the Terraform Azure provider, see [Terraform Registry Azure Resource Manager Provider](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs).
-* To learn more about network configuration options, see [Secure Azure Machine Learning workspace resources using virtual networks (VNets)](/azure/machine-learning/how-to-network-security-overview).
-* For alternative Azure Resource Manager template-based deployments, see [Deploy resources with Resource Manager templates and Resource Manager REST API](../azure-resource-manager/templates/deploy-rest.md).
+* To learn more about network configuration options, see [Secure Azure Machine Learning workspace resources using virtual networks (VNets)](./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-resource-manager/templates/deploy-rest.md).

articles/machine-learning/how-to-network-security-overview.md

@@ -32,7 +32,7 @@ Secure Azure Machine Learning workspace resources and compute environments using
 
 This article assumes that you have familiarity with the following topics:
 + [Azure Virtual Networks](../virtual-network/virtual-networks-overview.md)
-+ [IP networking](../virtual-network/public-ip-addresses.md)
++ [IP networking](../virtual-network/ip-services/public-ip-addresses.md)
 + [Azure Machine Learning workspace with private endpoint](how-to-configure-private-link.md)
 + [Network Security Groups (NSG)](../virtual-network/network-security-groups-overview.md)
 + [Network firewalls](../firewall/overview.md)
@@ -168,7 +168,7 @@ You can secure the workspace behind a VNet using a private endpoint and still al
 After securing the workspace with a private endpoint, use the following steps to enable clients to develop remotely using either the SDK or Azure Machine Learning studio:
 
 1. [Enable public access](how-to-configure-private-link.md#enable-public-access) to the workspace.
-1. [Configure the Azure Storage firewall](/azure/storage/common/storage-network-security?toc=%2Fazure%2Fstorage%2Fblobs%2Ftoc.json#grant-access-from-an-internet-ip-range) to allow communication with the IP address of clients that connect over the public internet.
+1. [Configure the Azure Storage firewall](../storage/common/storage-network-security.md?toc=%2fazure%2fstorage%2fblobs%2ftoc.json#grant-access-from-an-internet-ip-range) to allow communication with the IP address of clients that connect over the public internet.
 
 ## Optional: enable studio functionality
 

articles/machine-learning/how-to-safely-rollout-managed-endpoints.md

@@ -150,5 +150,5 @@ If you are not going use the deployment, you should delete it with:
 - [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview)
 - [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md)
 - [Managed online endpoints SKU list (preview)](reference-managed-online-endpoints-vm-sku-list.md)
-- [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
+- [Troubleshooting managed online endpoints deployment and scoring (preview)](./how-to-troubleshoot-online-endpoints.md)
 - [Managed online endpoints (preview) YAML reference](reference-yaml-endpoint-managed-online.md)

articles/machine-learning/how-to-secure-workspace-vnet.md

@@ -115,7 +115,7 @@ Azure Machine Learning supports storage accounts configured to use either a priv
     > When configuring a storage account that is **not** the default storage, select the **Target subresource** type that corresponds to the storage account you want to add.
 
 1. After creating the private endpoints for thee sub-resources, select the __Firewalls and virtual networks__ tab under __Networking__ for the storage account.
-1. Select __Selected networks__, and then under __Resource instances__, select `Microsoft.MachineLearningServices/Workspace` as the __Resource type__. Select your workspace using __Instance name__. For more information, see [Trusted access based on system-assigned managed identity](/azure/storage/common/storage-network-security#trusted-access-based-on-system-assigned-managed-identity).
+1. Select __Selected networks__, and then under __Resource instances__, select `Microsoft.MachineLearningServices/Workspace` as the __Resource type__. Select your workspace using __Instance name__. For more information, see [Trusted access based on system-assigned managed identity](../storage/common/storage-network-security.md#trusted-access-based-on-system-assigned-managed-identity).
 
     > [!TIP]
     > Alternatively, you can select __Allow Azure services on the trusted services list to access this storage account__ to more broadly allow access from trusted services. For more information, see [Configure Azure Storage firewalls and virtual networks](../storage/common/storage-network-security.md#trusted-microsoft-services).
@@ -138,7 +138,7 @@ Azure Machine Learning supports storage accounts configured to use either a priv
     > [!IMPORTANT]
     > The storage account must be in the same virtual network and subnet as the compute instances or clusters used for training or inference.
 
-1. Under __Resource instances__, select `Microsoft.MachineLearningServices/Workspace` as the __Resource type__ and select your workspace using __Instance name__. For more information, see [Trusted access based on system-assigned managed identity](/azure/storage/common/storage-network-security#trusted-access-based-on-system-assigned-managed-identity).
+1. Under __Resource instances__, select `Microsoft.MachineLearningServices/Workspace` as the __Resource type__ and select your workspace using __Instance name__. For more information, see [Trusted access based on system-assigned managed identity](../storage/common/storage-network-security.md#trusted-access-based-on-system-assigned-managed-identity).
 
     > [!TIP]
     > Alternatively, you can select __Allow Azure services on the trusted services list to access this storage account__ to more broadly allow access from trusted services. For more information, see [Configure Azure Storage firewalls and virtual networks](../storage/common/storage-network-security.md#trusted-microsoft-services).
@@ -166,7 +166,7 @@ Azure key vault can be configured to use either a private endpoint or service en
 
 # [Private endpoint](#tab/pe)
 
-For information on using a private endpoint with Azure Key Vault, see [Integrate Key Vault with Azure Private Link](/azure/key-vault/general/private-link-service#establish-a-private-link-connection-to-key-vault-using-the-azure-portal).
+For information on using a private endpoint with Azure Key Vault, see [Integrate Key Vault with Azure Private Link](../key-vault/general/private-link-service.md#establish-a-private-link-connection-to-key-vault-using-the-azure-portal).
 
 
 # [Service endpoint](#tab/se)
@@ -182,7 +182,7 @@ For information on using a private endpoint with Azure Key Vault, see [Integrate
 
     :::image type="content" source="./media/how-to-enable-virtual-network/key-vault-firewalls-and-virtual-networks-page.png" alt-text="The Firewalls and virtual networks section in the Key Vault pane":::
 
-For more information, see [Configure Azure Key Vault network settings](/azure/key-vault/general/how-to-azure-key-vault-network-security).
+For more information, see [Configure Azure Key Vault network settings](../key-vault/general/how-to-azure-key-vault-network-security.md).
 
 ---
 
@@ -302,4 +302,4 @@ This article is part of a series on securing an Azure Machine Learning workflow.
 * [Secure the inference environment](how-to-secure-inferencing-vnet.md)
 * [Enable studio functionality](how-to-enable-studio-virtual-network.md)
 * [Use custom DNS](how-to-custom-dns.md)
-* [Use a firewall](how-to-access-azureml-behind-firewall.md)
+* [Use a firewall](how-to-access-azureml-behind-firewall.md)

articles/machine-learning/how-to-use-managed-online-endpoint-studio.md

@@ -164,6 +164,6 @@ In this article, you learned how to use Azure Machine Learning managed online en
 - [How to deploy managed online endpoints with the Azure CLI](how-to-deploy-managed-online-endpoints.md)
 - [Deploy models with REST (preview)](how-to-deploy-with-rest.md)
 - [How to monitor managed online endpoints](how-to-monitor-online-endpoints.md)
-- [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
+- [Troubleshooting managed online endpoints deployment and scoring (preview)](./how-to-troubleshoot-online-endpoints.md)
 - [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md)
 - [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview)

articles/machine-learning/reference-yaml-endpoint-managed-online.md

@@ -80,4 +80,4 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 
 - [Install and use the CLI (v2)](how-to-configure-cli.md)
 - Learn how to [deploy a model with a managed online endpoint](how-to-deploy-managed-online-endpoints.md)
-- [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md)
+- [Troubleshooting managed online endpoints deployment and scoring (preview)](./how-to-troubleshoot-online-endpoints.md)

articles/machine-learning/tutorial-deploy-managed-endpoints-using-system-managed-identity.md

@@ -117,7 +117,7 @@ Check the status of the endpoint with the following.
 
 ::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint-access-resource-sai.sh" id="check_endpoint_Status" :::
 
-If you encounter any issues, see [Troubleshooting managed online endpoints deployment and scoring (preview)](how-to-troubleshoot-managed-online-endpoints.md).
+If you encounter any issues, see [Troubleshooting managed online endpoints deployment and scoring (preview)](./how-to-troubleshoot-online-endpoints.md).
 
 ## Give storage permission to system-assigned managed identity
 
@@ -195,4 +195,4 @@ In this Azure Machine Learning tutorial, you used the machine learning CLI for t
 * For more on costs, see [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md).
 * For more on deployment, see [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md).
 * For information on monitoring endpoints, see [Monitor managed online endpoints (preview)](how-to-monitor-online-endpoints.md).
-* For information on limitations for managed endpoints, see [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview).
+* For information on limitations for managed endpoints, see [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints-preview).