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Copy file name to clipboardExpand all lines: articles/ai-foundry/how-to/configure-private-link.md
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@@ -10,6 +10,7 @@ ms.date: 01/15/2025
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ms.reviewer: meerakurup
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ms.author: larryfr
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author: Blackmist
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zone_pivot_groups: azure-portal-and-cli
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# Customer intent: As an admin, I want to configure a private link for hub so that I can secure my hubs.
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---
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@@ -25,7 +26,6 @@ You get several hub default resources in your resource group. You need to config
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- Establish private endpoint connection to hub default resources. You need to have both a blob and file private endpoint for the default storage account.
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- If your storage account is private, [assign roles](#private-storage-configuration) to allow access.
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-
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## Prerequisites
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* You must have an existing Azure Virtual Network to create the private endpoint in.
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## Create a hub that uses a private endpoint
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If you are creating a new hub, use the following tabs to select how you are creating the hub (Azure portal or Azure CLI.) Each of these methods __requires an existing virtual network__:
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If you are creating a new hub, use the following methods to create the hub (Azure portal or Azure CLI). Each of these methods __requires an existing virtual network__:
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# [Azure portal](#tab/azure-portal)
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:::zone pivot="azure-portal"
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> [!NOTE]
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> The information in this document is only about configuring a private link. For a walkthrough of creating a secure hub in the portal, see [Create a secure hub in the Azure portal](create-secure-ai-hub.md).
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1. Input required fields. When selecting the __Region__, select the same region as your virtual network.
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# [Azure CLI](#tab/cli)
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:::zone-end
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:::zone pivot="cli"
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> [!NOTE]
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> The information in this section doesn't cover basic hub configuration. For more information, see [Create a hub using the Azure CLI](./develop/create-hub-project-sdk.md?tabs=azurecli).
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Use one of the following methods to add a private endpoint to an existing hub:
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# [Azure portal](#tab/azure-portal)
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:::zone pivot="azure-portal"
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1. From the [Azure portal](https://portal.azure.com), select your hub.
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1. From the left side of the page, select __Settings__, __Networking__, and then select the __Private endpoint connections__ tab. Select __+ Private endpoint__.
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1. When going through the forms to create a private endpoint, be sure to:
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- From __Basics__, select the same the __Region__ as your virtual network.
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- From __Basics__, select the same __Region__ as your virtual network.
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- From __Resource__, select `amlworkspace` as the __target sub-resource__.
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- From the __Virtual Network__ form, select the virtual network and subnet that you want to connect to.
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1. After populating the forms with any additional network configurations you require, use the __Review + create__ tab to review your settings and select __Create__ to create the private endpoint.
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# [Azure CLI](#tab/cli)
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:::zone-end
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:::zone pivot="cli"
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Use the [Azure networking CLI commands](/cli/azure/network/private-endpoint#az-network-private-endpoint-create) to create a private link endpoint for the hub.
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# Add privatelink.api.azureml.ms
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az network private-dns zone create \
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-g <resource-group-name> \
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--name 'privatelink.api.azureml.ms'
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--name privatelink.api.azureml.ms
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az network private-dns link vnet create \
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-g <resource-group-name> \
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--zone-name 'privatelink.api.azureml.ms' \
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--zone-name privatelink.api.azureml.ms \
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--name <link-name> \
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--virtual-network <vnet-name> \
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--registration-enabled false
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-g <resource-group-name> \
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--endpoint-name <private-endpoint-name> \
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--name myzonegroup \
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--private-dns-zone 'privatelink.api.azureml.ms' \
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--zone-name 'privatelink.api.azureml.ms'
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--private-dns-zone privatelink.api.azureml.ms \
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--zone-name privatelink.api.azureml.ms
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# Add privatelink.notebooks.azure.net
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az network private-dns zone create \
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-g <resource-group-name> \
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--name 'privatelink.notebooks.azure.net'
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--name privatelink.notebooks.azure.net
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az network private-dns link vnet create \
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-g <resource-group-name> \
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--zone-name 'privatelink.notebooks.azure.net' \
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--zone-name privatelink.notebooks.azure.net \
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--name <link-name> \
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--virtual-network <vnet-name> \
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--registration-enabled false
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To remove a private endpoint, use the following information:
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# [Azure portal](#tab/azure-portal)
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:::zone pivot="azure-portal"
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1. From the [Azure portal](https://portal.azure.com), select your hub.
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1. From the left side of the page, select __Settings__, __Networking__, and then select the __Private endpoint connections__ tab.
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1. Select the endpoint to remove and then select __Remove__.
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:::image type="content" source="../media/how-to/network/remove-private-endpoint.png" alt-text="Screenshot of a selected private endpoint with the remove option highlighted.":::
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# [Azure CLI](#tab/cli)
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:::zone-end
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:::zone pivot="cli"
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When using the Azure CLI, use the following command to remove the private endpoint:
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```azurecli
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az network private-endpoint delete \
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--name <private-endpoint-name> \
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--resource-group <resource-group-name> \
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--resource-group <resource-group-name>
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```
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:::zone-end
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---
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## Enable public access
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To enable public access, use the following steps:
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# [Azure portal](#tab/azure-portal)
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:::zone pivot="azure-portal"
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1. From the [Azure portal](https://portal.azure.com), select your hub.
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1. From the left side of the page, select __Networking__ and then select the __Public access__ tab.
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1. Select __Enabled from all networks__, and then select __Save__.
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# [Azure CLI](#tab/cli)
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:::zone-end
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:::zone pivot="cli"
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Use the following Azure CLI command to enable public access:
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@@ -255,8 +250,9 @@ If you receive an error that the `ml` command isn't found, use the following com
Copy file name to clipboardExpand all lines: articles/ai-services/content-moderator/video-moderation-api.md
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@@ -46,7 +46,7 @@ Once you've done this, you can use the video moderation media processor in two d
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## Use Azure Media Services Explorer
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The Azure Media Services Explorer is a user-friendly frontend for AMS. Use it to browse your AMS account, upload videos, and scan content with the Content Moderator media processor. Download and install it from [GitHub](https://github.com/Azure/Azure-Media-Services-Explorer/releases), or see the [Azure Media Services Explorer blog post](https://azure.microsoft.com/blog/managing-media-workflows-with-the-new-azure-media-services-explorer-tool/) for more information.
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The Azure Media Services Explorer is a user-friendly frontend for AMS. Use it to browse your AMS account, upload videos, and scan content with the Content Moderator media processor. Download and install it from GitHub, or see the [Azure Media Services Explorer blog post](https://azure.microsoft.com/blog/managing-media-workflows-with-the-new-azure-media-services-explorer-tool/) for more information.
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Copy file name to clipboardExpand all lines: articles/ai-services/language-service/question-answering/includes/sdk-python.md
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@@ -112,7 +112,7 @@ Confidence Score: 0.9185
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The confidence score returns a value between 0 and 1. You can think of this like a percentage and multiply by 100 so a confidence score of 0.9185 means custom question answering is 91.85% confident this is the correct answer to the question based on the project.
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If you want to exclude answers where the confidence score falls below a certain threshold, you can modify the [AnswerOptions](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-questionanswering/1.0.0/azure.ai.language.questionanswering.models.html#azure.ai.language.questionanswering.models.AnswersOptions) to add the `confidence_threshold` parameter.
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If you want to exclude answers where the confidence score falls below a certain threshold, you can modify the AnswerOptions to add the `confidence_threshold` parameter.
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```python
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output = client.get_answers(
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Confidence Score: 0.9254655838012695
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```
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In this case, we iterate through all responses and only return the response with the highest confidence score that is greater than 0.9. To understand more about the options available with get_answers_from_text, review the [AnswersFromTextOptions parameters](https://azuresdkdocs.blob.core.windows.net/$web/python/azure-ai-language-questionanswering/1.0.0/azure.ai.language.questionanswering.models.html#azure.ai.language.questionanswering.models.AnswersFromTextOptions).
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In this case, we iterate through all responses and only return the response with the highest confidence score that is greater than 0.9. To understand more about the options available with get_answers_from_text, review the AnswersFromTextOptions parameters.
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