Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr

Author: s-polly

articles/ai-foundry/concepts/models-featured.md

@@ -181,8 +181,8 @@ Meta Llama models and tools are a collection of pretrained and fine-tuned genera
 
 | Model  | Type | Capabilities |
 | ------ | ---- | ------------ |
-| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** Yes <br /> - **Response formats:** Text |
-| [Llama 4-Maverick-17B-128E-Instruct-FP8](https://aka.ms/aifoundry/landing/llama-4-maverick-17b-128e-instruct-fp8) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** Yes <br /> - **Response formats:** Text |
+| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
+| [Llama 4-Maverick-17B-128E-Instruct-FP8](https://aka.ms/aifoundry/landing/llama-4-maverick-17b-128e-instruct-fp8) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.3-70B-Instruct](https://ai.azure.com/explore/models/Llama-3.3-70B-Instruct/version/4/registry/azureml-meta) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.2-90B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-90B-Vision-Instruct/version/1/registry/azureml-meta) | [chat-completion (with images)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.2-11B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-11B-Vision-Instruct/version/1/registry/azureml-meta) | [chat-completion (with images)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |

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

@@ -9,7 +9,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: how-to
-ms.date: 11/07/2024
+ms.date: 06/06/2025
 ms.reviewer: deeikele
 ms.author: sgilley
 author: sdgilley

articles/ai-foundry/includes/create-hub-project-simple.md

@@ -12,27 +12,17 @@ ms.custom: include, build-2024, ignite-2024
 
  To create a [!INCLUDE [hub-project-name](hub-project-name.md)] in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs), follow these steps:
 
- [!INCLUDE [tip-left-pane](../includes/tip-left-pane.md)]
- 
- 1. Sign in to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs). 
- 1. Select any existing project if you aren't already in one.
- 
-    > [!IMPORTANT]
-    > You must have an existing [!INCLUDE [fdp-project-name](fdp-project-name.md)] or [!INCLUDE [hub-project-name](hub-project-name.md)] before you can follow these steps. If you don't have an existing project, follow the steps in the [Quickstart: Get started with Azure AI Foundry](../quickstarts/get-started-code.md) to create your first [!INCLUDE [fdp-project-name](fdp-project-name.md)].
+1. Sign in to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
 
- 1. At the bottom of the left pane, select **Management center**.
- 1. At the top of the page, select **All resources**.
- 1. Select **Create**.
- 
-    :::image type="content" source="../media/how-to/projects/management-center-create.png" alt-text="Screenshot shows the management center where you can create a hub based project.":::
+1. [!INCLUDE [create-project-access](create-project-access.md)]
 
- 1. Select **AI hub resource**.
- 1. Enter a name for the project.
- 1. If you have a hub, you'll see the one you most recently used selected.  
-     * If you have access to more than one hub, you can select a different hub from the dropdown.
-     * If you want to create a new one, select **Create new hub** and supply a  name.  
- 
-         :::image type="content" source="../media/how-to/projects/projects-create-details.png" alt-text="Screenshot of the project details page within the create project dialog." lightbox="../media/how-to/projects/projects-create-details.png":::
- 
- 1. If you don't have a hub, a default one is created for you. 
- 1. Select **Create**. 
+1. Select **AI hub resource**, then select **Next**.
+1. Enter a name for the project.
+1. If you have a hub, you'll see the one you most recently used selected.  
+   * If you have access to more than one hub, you can select a different hub from the dropdown.
+   * If you want to create a new one, select **Create new hub** and supply a  name.  
+
+      :::image type="content" source="../media/how-to/projects/projects-create-details.png" alt-text="Screenshot of the project details page within the create project dialog." lightbox="../media/how-to/projects/projects-create-details.png":::
+
+1. If you don't have a hub, a default one is created for you. 
+1. Select **Create**. 

articles/ai-foundry/includes/create-project-access.md

@@ -0,0 +1,23 @@
+---
+title: Include file
+description: Include file
+author: sdgilley
+ms.reviewer: sgilley
+ms.author: sgilley
+ms.service: azure-ai-foundry
+ms.topic: include
+ms.date: 06/05/2025
+ms.custom: include
+---
+
+What you do next depends on where you are:
+
+* **If you don't have any existing projects**: Follow the steps in [Quickstart: Get started with Azure AI Foundry](../quickstarts/get-started-code.md) to create your first project.
+
+* **If you're in a project**: Select the project breadcrumb, then select **Create new resource**.
+
+    :::image type="content" source="../media/how-to/projects/create-new-resource.png" alt-text="Screenshot shows creating a new project from a breadcrumb.":::
+
+* **If you're not in a project**: Select **Create new** in the top right to create a new [!INCLUDE [fdp-project-name-plural](fdp-project-name.md)]
+
+    :::image type="content" source="../media/how-to/projects/create-new.png" alt-text="Screenshot shows how to create a new project in Azure AI Foundry.":::

articles/ai-foundry/includes/create-project-fdp.md

@@ -63,19 +63,12 @@ Use the following tabs to select the method you plan to use to create a [!INCLUD
 
 To create a [!INCLUDE [fdp-project-name](fdp-project-name.md)] in [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs), follow these steps:
 
-1. Go to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
+1. Sign in to [Azure AI Foundry](https://ai.azure.com/?cid=learnDocs).
 
-2. What you do next depends on where you are:
+1. [!INCLUDE [create-project-access](create-project-access.md)]
 
-    * **If you don't have any existing projects**: Follow the steps in [Quickstart: Get started with Azure AI Foundry](../quickstarts/get-started-code.md) to create your first project.
-
-    * **If you're in a project**: Select the project breadcrumb, then select **Create new resource**.
-    
-        :::image type="content" source="../media/how-to/projects/create-new-resource.png" alt-text="Screenshot shows creating a new project from a breadcrumb.":::
-    
-    * **If you're not in a project**: Select **Create new** in the top right to create a new [!INCLUDE [fdp-project-name-plural](fdp-project-name.md)]
-    
-        :::image type="content" source="../media/how-to/projects/create-new.png" alt-text="Screenshot shows how to create a new project in Azure AI Foundry.":::
+1. Select **Azure AI Foundry resource**, then select **Next**.
+1. Provide a name for your project and select **Create**.  Or see next section for advanced options.
 
 ### Advanced options
 

articles/ai-foundry/model-inference/concepts/models.md

@@ -97,6 +97,16 @@ Meta Llama models and tools are a collection of pretrained and fine-tuned genera
 
 See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=meta).
 
+### xAI
+
+xAI's Grok 3 and Grok 3 Mini models are designed to excel in various enterprise domains. Grok 3, a non-reasoning model pre-trained by the Colossus datacenter, is tailored for business use cases such as data extraction, coding, and text summarization, with exceptional instruction-following capabilities. It supports a 131,072 token context window, allowing it to handle extensive inputs while maintaining coherence and depth, and is particularly adept at drawing connections across domains and languages. On the other hand, Grok 3 Mini is a lightweight reasoning model trained to tackle agentic, coding, mathematical, and deep science problems with test-time compute. It also supports a 131,072 token context window for understanding codebases and enterprise documents, and excels at using tools to solve complex logical problems in novel environments, offering raw reasoning traces for user inspection with adjustable thinking budgets.
+
+| Model  | Type | Tier | Capabilities |
+| ------ | ---- | --- | ------------ |
+| [grok-3](https://ai.azure.com/explore/models/grok-3/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
+| [grok-3-mini](https://ai.azure.com/explore/models/grok-3-mini/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
+
+
 ## Models from Partners and Community
 
 Models from Partners and Community available for deployment with pay-as-you-go billing (for example, Cohere models) are offered by the model provider but hosted in Microsoft-managed Azure infrastructure and accessed via API in the Azure AI Foundry. Model providers define the license terms and set the price for use of their models, while Azure AI Foundry manages the hosting infrastructure.
@@ -210,15 +220,6 @@ See [this model collection in Azure AI Foundry portal](https://ai.azure.com/expl
 | ------ | ---- | --- | ------------ |
 | [tsuzumi-7b](https://ai.azure.com/explore/models/Tsuzumi-7b/version/1/registry/azureml-nttdata) | chat-completion | Global standard | - **Input:** text (8,192 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Languages:** `en` and `jp` <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 
-### xAI
-
-xAI's Grok 3 and Grok 3 Mini models are designed to excel in various enterprise domains. Grok 3, a non-reasoning model pre-trained by the Colossus datacenter, is tailored for business use cases such as data extraction, coding, and text summarization, with exceptional instruction-following capabilities. It supports a 131,072 token context window, allowing it to handle extensive inputs while maintaining coherence and depth, and is particularly adept at drawing connections across domains and languages. On the other hand, Grok 3 Mini is a lightweight reasoning model trained to tackle agentic, coding, mathematical, and deep science problems with test-time compute. It also supports a 131,072 token context window for understanding codebases and enterprise documents, and excels at using tools to solve complex logical problems in novel environments, offering raw reasoning traces for user inspection with adjustable thinking budgets.
-
-| Model  | Type | Tier | Capabilities |
-| ------ | ---- | --- | ------------ |
-| [grok-3](https://ai.azure.com/explore/models/grok-3/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
-| [grok-3-mini](https://ai.azure.com/explore/models/grok-3-mini/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
-
 ## Open and protected models
 
 The [Azure AI model catalog](../../../ai-studio/how-to/model-catalog-overview.md) offers a larger selection of models, from a bigger range of providers. As opposite to Azure AI Foundry Models where models are provided as APIs, these models might require you to host them on your infrastructure, including the creation of an AI hub and project, and providing the underlying compute quota to host the models. 

articles/ai-foundry/what-is-azure-ai-foundry.md

@@ -58,7 +58,8 @@ This table summarizes features available in the two project types:
 | Capability | [!INCLUDE [fdp](includes/fdp-project-name.md)] | [!INCLUDE[hub](includes/hub-project-name.md)] |
 | --- | --- | --- |
 | Agents | ✅ (GA) | ✅ (Preview only) |
-| Azure OpenAI models | ✅ (Native support) | Available via connections |
+| Azure AI Foundry Models including Azure OpenAI models | ✅ (Native support) | Available via connections |
+| AI Foundry API to work with agents and across models| ✅ (Native support) | Available via connections |
 | Project files (directly upload files and start experimenting) | ✅ | |
 | Project-level isolation of files and outputs | ✅ | ✅|
 | Evaluations | ✅ | ✅ |

articles/ai-services/language-service/conversational-language-understanding/concepts/data-formats.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/21/2024
+ms.date: 06/05/2025
 ms.author: lajanuar
 ms.custom: language-service-custom-clu
 ---

articles/ai-services/language-service/custom-text-classification/concepts/evaluation-metrics.md

@@ -7,7 +7,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: conceptual
-ms.date: 11/21/2024
+ms.date: 6/6/2025
 ms.author: lajanuar
 ms.custom: language-service-custom-classification
 ---
@@ -26,12 +26,12 @@ Model evaluation is triggered automatically after training is completed successf
 
     `Recall = #True_Positive / (#True_Positive + #False_Negatives)`
 
-* **F1 score**: The F1 score is a function of Precision and Recall. It's needed when you seek a balance between Precision and Recall.
+* **F1 score**: The F1 score is a function of precision and recall. It's needed when you seek a balance between precision and recall.
 
     `F1 Score = 2 * Precision * Recall / (Precision + Recall)` <br> 
 
 >[!NOTE]
-> Precision, recall and F1 score are calculated for each class separately (*class-level* evaluation) and for the model collectively (*model-level* evaluation).
+> Precision, recall, and F1 score are calculated for each class separately (*class-level* evaluation) and for the model collectively (*model-level* evaluation).
 ## Model-level and Class-level evaluation metrics
 
 The definitions of precision, recall, and evaluation are the same for both class-level and model-level evaluations. However, the count of *True Positive*, *False Positive*, and *False Negative* differ as shown in the following example.
@@ -89,7 +89,7 @@ The below sections use the following example dataset:
 **F1 Score** = `2 * Precision * Recall / (Precision + Recall) =  (2 * 0.8 * 0.67) / (0.8 + 0.67) = 0.73`
 
 > [!NOTE] 
-> For single-label classification models, the count of false negatives and false positives are always equal. Custom single-label classification models always predict one class for each document. If the prediction is not correct, FP count of the predicted class increases by one and FN of the actual class increases by one, overall count of FP and FN for the model will always be equal. This is not the case for multi-label classification, because failing to predict one of the classes of a document is counted as a false negative. 
+> For single-label classification models, the number of false negatives and false positives are always equal. Custom single-label classification models always predict one class for each document. If the prediction is not correct, FP count of the predicted class increases by one and FN of the actual class increases by one, overall count of FP and FN for the model will always be equal. This is not the case for multi-label classification, because failing to predict one of the classes of a document is counted as a false negative. 
 ## Interpreting class-level evaluation metrics
 
 So what does it actually mean to have a high precision or a high recall for a certain class?