Merge pull request #6591 from MicrosoftDocs/main

Auto Publish – main to live - 2025-08-14 22:04 UTC

Author: learn-build-service-prod[bot]

articles/ai-foundry/agents/how-to/tools/browser-automation-samples.md

@@ -6,7 +6,7 @@ services: azure-ai-agent-service
 manager: nitinme
 ms.service: azure-ai-agent-service
 ms.topic: how-to
-ms.date: 08/12/2025
+ms.date: 08/14/2025
 author: aahill
 ms.author: aahi
 ms.custom: azure-ai-agents
@@ -18,7 +18,7 @@ Use this article to find step-by-step instructions and code samples for using th
 
 ## Prerequisites
 
-* The requirements in the [Browser Automation overview](./deep-research.md).
+* The requirements in the [Browser Automation overview](./browser-automation.md#setup).
 * Your Azure AI Foundry Project endpoint.
 
     [!INCLUDE [endpoint-string-portal](../../includes/endpoint-string-portal.md)]

articles/ai-foundry/how-to/create-azure-ai-hub-template.md

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - hub-only
 ms.topic: how-to
-ms.date: 04/29/2025
+ms.date: 08/14/2025
 ms.reviewer: deeikele
 ms.author: sgilley
 author: sdgilley

articles/ai-foundry/how-to/create-azure-ai-project-template.md

@@ -9,15 +9,12 @@ ms.service: azure-ai-foundry
 ms.topic: quickstart-bicep
 ms.custom: 
   - subject-bicepqs
-  - hub-only
-ms.date: 04/29/2025
+ms.date: 08/14/2025
 # Customer intent: As a DevOps person, I need to automate or customize the creation of a hub by using templates.
 ---
 
 # Quickstart: Create an Azure AI Foundry project using a Bicep file
 
-[!INCLUDE [hub-only-alt](../includes/uses-fdp-only-alt.md)]
-
 Use a [Microsoft Bicep](/azure/azure-resource-manager/bicep/overview) file (template) to create an [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs) project. A template makes it easy to create resources as a single, coordinated operation. A Bicep file is a text document that defines the resources that are needed for a deployment. It might also specify deployment parameters. Parameters are used to provide input values when using the file to deploy resources.
 
 ## Prerequisites
@@ -69,13 +66,13 @@ az deployment group create --resource-group exampleRG --template-file main.bicep
 
 ```azurepowershell
 New-AzResourceGroup -Name exampleRG -Location eastus
-New-AzResourceGroupDeployment -ResourceGroupName exampleRG -TemplateFile main.bicep -aiHubName myai -aiProjectName myai-proj
+New-AzResourceGroupDeployment -ResourceGroupName exampleRG -TemplateFile main.bicep -aiFoundryName myai -aiProjectName myai-proj
 ```
 
 ---
 
 > [!NOTE]
-> Replace `myai` with the name of your resource.
+> Replace `myai` with the name of your resource. 
 
 When the deployment finishes, you should see a message indicating the deployment succeeded.
 
@@ -86,7 +83,7 @@ Use the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs) to view t
 # [Azure CLI](#tab/cli)
 
 ```azurecli
-az resource list --name exampleRG
+az resource list --resource-group exampleRG
 ```
 
 # [Azure PowerShell](#tab/powershell)

articles/ai-foundry/how-to/create-manage-compute.md

@@ -9,7 +9,7 @@ ms.custom:
   - ignite-2024
   - hub-only
 ms.topic: how-to
-ms.date: 06/06/2025
+ms.date: 08/14/2025
 ms.reviewer: deeikele
 ms.author: sgilley
 author: sdgilley

articles/ai-foundry/how-to/develop/create-hub-project-sdk.md

@@ -5,7 +5,7 @@ description: This article provides instructions on how to create an Azure AI Fou
 ms.service: azure-ai-foundry
 ms.custom: build-2024, devx-track-azurecli
 ms.topic: how-to
-ms.date: 06/24/2025
+ms.date: 08/14/2025
 ms.reviewer: dantaylo
 ms.author: sgilley
 author: sdgilley

articles/ai-services/language-service/conversational-language-understanding/how-to/create-project.md

@@ -17,9 +17,9 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 
 > [!NOTE]
 >
-> If you already have an Azure AI Language or multi-service resource—whether used on its own or through Language Studio—you can continue to use those existing Language resources within the Azure AI Foundry portal. For more information, see [How to use Azure AI services in the Azure AI Foundry portal](../../../../ai-services/connect-services-ai-foundry-portal.md).
->
-> In Azure AI Foundry, you set up a fine-tuning task to serve as your workspace when customizing your CLU model. Previously, a **fine-tuning task** was referred to as a **CLU project**. You might encounter both terms used interchangeably in older CLU documentation.
+> * If you already have an Azure AI Language or multi-service resource—whether used on its own or through Language Studio—you can continue to use those existing Language resources within the Azure AI Foundry portal. For more information, see [How to use Azure AI services in the Azure AI Foundry portal](../../../../ai-services/connect-services-ai-foundry-portal.md).
+> * In Azure AI Foundry, a fine-tuning task serves as your workspace when customizing your CLU model. Previously, a **fine-tuning task** was referred to as a **CLU project**. You might encounter both terms used interchangeably in older CLU documentation.
+> * We highly recommend that you use an Azure AI Foundry resource in the AI Foundry; however, you can also follow these instructions using a Language resource.
 >
 
 ## Prerequisites
@@ -29,10 +29,6 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 *  An [Azure AI Foundry multi-service resource](../../../multi-service-resource.md). For more information, *see* [Configure an Azure AI Foundry resource](configure-azure-resources.md#option-1-configure-an-azure-ai-foundry-resource). Alternately, you can use an [Azure AI Language resource](https://portal.azure.com/?Microsoft_Azure_PIMCommon=true#create/Microsoft.CognitiveServicesTextAnalytics).
 * A Foundry project created in the Azure AI Foundry. For more information, *see* [Create an AI Foundry project](../../../../ai-foundry/how-to/create-projects.md).
 
-> [!NOTE]
->
-> We highly recommend that you use an Azure AI Foundry resource in the AI Foundry; however, you can also follow these instructions using a Language resource.
-
 ## Create a CLU fine-tuning task project
 
  To create a CLU fine-tuning task project, you first configure your environment and then create a fine-tuning task, which serves as your workspace for customizing your CLU model.
@@ -71,7 +67,7 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 
 1. From **Create service fine-tuning** window, choose the **Conversational language understanding** tab then select **Next**.
 
-    :::image type="content" source="../media/select-project.png" alt-text="Screenshot of conversational language understanding tab in the Azure AI Foundry.":::
+    :::image type="content" source="../media/select-project.png" alt-text="Screenshot of conversational language understanding selection card in the Azure AI Foundry.":::
 
 1. In **Create CLU fine tuning task** window, select your **Connected service** from the drop-down menu, then complete the **Name** and **Language** fields. If you're using the free **Standard Training** mode, select **English** for the language field.
 
@@ -84,7 +80,7 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
    > * **Advanced training** includes longer training durations and is supported for English, other languages, and multilingual projects.
    > * For more information, *see* [Training modes](train-model.md#training-modes).
 
-1. Once the task creation is complete, select the task from the AI Service fine-tuning window to arrive at the Getting started with fine-tuning page.
+1. Once the task creation is complete, select the task from the AI Service fine-tuning window to arrive at the **Getting started with fine-tuning** page.
 
    :::image type="content" source="../media/create-project/getting-started-fine-tuning.png" alt-text="Screenshot of the getting started with fine-tuning page in the Azure AI Foundry." lightbox="../media/create-project/getting-started-fine-tuning.png":::
 

articles/ai-services/language-service/conversational-language-understanding/how-to/train-model.md

@@ -54,9 +54,10 @@ We recommend that you introduce casing and punctuation diversity in the training
 
 ## Data splitting
 
-Before you start the training process, labeled utterances in your project are divided into a training set and a testing set. Each one of them serves a different function.
-The **training set** is used in training the model, the set from which the model learns the labeled utterances.
-The **testing set** is a blind set that isn't introduced to the model during training but only during evaluation.
+Before you start the training process, labeled utterances in your project are divided into a training set and a testing set. Each one of them serves a different function:
+
+* The **training set** is used in training the model, the set from which the model learns the labeled utterances.
+* The **testing set** is a blind set that isn't introduced to the model during training but only during evaluation.
 
 After the model is trained successfully, the model can be used to make predictions from the utterances in the testing set. These predictions are used to calculate [evaluation metrics](../concepts/evaluation-metrics.md).
 We recommend that you make sure that all your intents and entities are adequately represented in both the training and testing set.