Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr into tonye-release-branch-4

Author: tonyeiyalla

.acrolinx-config.edn

@@ -1,2 +1,2 @@
 {:allowed-branchname-matches ["main" "release-.*"]
- :allowed-filename-matches ["(?i)articles/(?:(?!active-directory/saas-apps/toc.yml|role-based-access-control/resource-provider-operations.md|.*policy/samples/|.*resource-graph/samples/))" "(?i)includes/(?:(?!policy/reference/|policy/standards/|resource-graph/samples/))"]}
+ :allowed-filename-matches ["articles"]}

articles/ai-foundry/concepts/model-benchmarks.md

@@ -9,8 +9,8 @@ ms.custom:
 ms.topic: concept-article
 ms.date: 04/04/2025
 ms.reviewer: changliu2
-ms.author: mopeakande
-author: msakande
+ms.author: lagayhar  
+author: lgayhardt
 ---
 
 # Model leaderboards in Azure AI Foundry portal (preview)

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

@@ -209,10 +209,12 @@ For more examples of how to use Meta Llama models, see the following examples:
 
 ## Microsoft
 
-Phi is a family of lightweight, state-of-the-art open models. These models were trained with Phi-3 datasets. The datasets include both synthetic data and the filtered, publicly available websites data, with a focus on high quality and reasoning-dense properties. The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
+Microsoft models include various model groups such as MAI models, Phi models, healthcare AI models, and more. To see all the available Microsoft models, view [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
+
 
 | Model  | Type | Capabilities |
 | ------ | ---- | ------------ |
+| [MAI-DS-R1](https://ai.azure.com/explore/models/MAI-DS-R1/version/1/registry/azureml) | [chat-completion with reasoning content](../model-inference/how-to/use-chat-reasoning.md?context=/azure/ai-foundry/context/context) | - **Input:** text (163,840 tokens) <br /> - **Output:** text (163,840 tokens) <br />  - **Tool calling:** No <br /> - **Response formats:** Text. |
 | [Phi-4-multimodal-instruct](https://ai.azure.com/explore/models/Phi-4-multimodal-instruct/version/1/registry/azureml) | [chat-completion (with image and audio content)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text, images, and audio (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Phi-4-mini-instruct](https://ai.azure.com/explore/models/Phi-4-mini-instruct/version/1/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Phi-4](https://ai.azure.com/explore/models/Phi-4/version/2/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (16,384 tokens) <br /> - **Output:** text (16,384 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
@@ -226,12 +228,9 @@ Phi is a family of lightweight, state-of-the-art open models. These models were
 | [Phi-3-medium-128k-instruct](https://ai.azure.com/explore/models/Phi-3-medium-128k-instruct/version/6/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Phi-3-medium-4k-instruct](https://ai.azure.com/explore/models/Phi-3-medium-4k-instruct/version/5/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (4,096 tokens) <br /> - **Output:** text (4,096 tokens) <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 
+#### Inference examples: Microsoft models
 
-See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
-
-#### Inference examples: Microsoft Phi
-
-For more examples of how to use Phi-3 family models, see the following examples:    
+For more examples of how to use Microsoft models, see the following examples:    
 
 | Description                               | Language          | Sample                                                          |    
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|    
@@ -242,6 +241,9 @@ For more examples of how to use Phi-3 family models, see the following examples:
 | Llama-Index                               | Python            | [Link](https://aka.ms/azureai/llamaindex)             |  
 
 
+See [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
+
+
 ## Mistral AI
 
 Mistral AI offers two categories of models, namely: 

articles/ai-foundry/how-to/benchmark-model-in-catalog.md

@@ -10,8 +10,8 @@ ms.topic: how-to
 ms.date: 04/07/2025
 ms.reviewer: changliu2
 reviewer: changliu2
-ms.author: mopeakande
-author: msakande
+ms.author: lagayhar  
+author: lgayhardt
 ---
 
 # Compare and select models using the model leaderboard in Azure AI Foundry portal (preview)

articles/ai-foundry/how-to/configure-private-link.md

@@ -10,6 +10,7 @@ ms.date: 01/15/2025
 ms.reviewer: meerakurup
 ms.author: larryfr
 author: Blackmist
+zone_pivot_groups: azure-portal-and-cli
 # Customer intent: As an admin, I want to configure a private link for hub so that I can secure my hubs.
 ---
 
@@ -25,7 +26,6 @@ You get several hub default resources in your resource group. You need to config
 - Establish private endpoint connection to hub default resources. You need to have both a blob and file private endpoint for the default storage account.
 - If your storage account is private, [assign roles](#private-storage-configuration) to allow access.
 
-
 ## Prerequisites
 
 * You must have an existing Azure Virtual Network to create the private endpoint in. 
@@ -37,9 +37,9 @@ You get several hub default resources in your resource group. You need to config
 
 ## Create a hub that uses a private endpoint
 
-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__:
+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__:
 
-# [Azure portal](#tab/azure-portal)
+:::zone pivot="azure-portal"
 
 > [!NOTE]
 > 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).
@@ -55,7 +55,9 @@ If you are creating a new hub, use the following tabs to select how you are crea
 
 1. Input required fields. When selecting the __Region__, select the same region as your virtual network.
 
-# [Azure CLI](#tab/cli)
+:::zone-end
+
+:::zone pivot="cli"
 
 > [!NOTE]
 > 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).
@@ -95,34 +97,17 @@ az network private-endpoint dns-zone-group create \
     --name myzonegroup \
     --private-dns-zone privatelink.api.azureml.ms \
     --zone-name privatelink.api.azureml.ms
-
-# Add privatelink.notebooks.azure.net
-az network private-dns zone create \
-    -g <resource-group-name> \
-    --name privatelink.notebooks.azure.net
-
-az network private-dns link vnet create \
-    -g <resource-group-name> \
-    --zone-name privatelink.notebooks.azure.net \
-    --name <link-name> \
-    --virtual-network <vnet-name> \
-    --registration-enabled false
-
-az network private-endpoint dns-zone-group add \
-    -g <resource-group-name> \
-    --endpoint-name <private-endpoint-name> \
-    --name myzonegroup \
-    --private-dns-zone privatelink.notebooks.azure.net \
-    --zone-name privatelink.notebooks.azure.net
 ```
 
+:::zone-end
+
 ---
 
 ## Add a private endpoint to a hub
 
 Use one of the following methods to add a private endpoint to an existing hub:
 
-# [Azure portal](#tab/azure-portal)
+:::zone pivot="azure-portal"
 
 1. From the [Azure portal](https://portal.azure.com), select your hub.
 1. From the left side of the page, select __Settings__, __Networking__, and then select the __Private endpoint connections__ tab. Select __+ Private endpoint__.
@@ -131,13 +116,15 @@ Use one of the following methods to add a private endpoint to an existing hub:
 
 1. When going through the forms to create a private endpoint, be sure to:
 
-    - From __Basics__, select the same the __Region__ as your virtual network.
+    - From __Basics__, select the same __Region__ as your virtual network.
     - From __Resource__, select `amlworkspace` as the __target sub-resource__.
     - From the __Virtual Network__ form, select the virtual network and subnet that you want to connect to.
 
 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.
 
-# [Azure CLI](#tab/cli)
+:::zone-end
+
+:::zone pivot="cli"
 
 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.
 
@@ -159,11 +146,11 @@ To create the private DNS zone entries for the workspace, use the following comm
 # Add privatelink.api.azureml.ms
 az network private-dns zone create \
     -g <resource-group-name> \
-    --name 'privatelink.api.azureml.ms'
+    --name privatelink.api.azureml.ms
 
 az network private-dns link vnet create \
     -g <resource-group-name> \
-    --zone-name 'privatelink.api.azureml.ms' \
+    --zone-name privatelink.api.azureml.ms \
     --name <link-name> \
     --virtual-network <vnet-name> \
     --registration-enabled false
@@ -172,17 +159,17 @@ az network private-endpoint dns-zone-group create \
     -g <resource-group-name> \
     --endpoint-name <private-endpoint-name> \
     --name myzonegroup \
-    --private-dns-zone 'privatelink.api.azureml.ms' \
-    --zone-name 'privatelink.api.azureml.ms'
+    --private-dns-zone privatelink.api.azureml.ms \
+    --zone-name privatelink.api.azureml.ms
 
 # Add privatelink.notebooks.azure.net
 az network private-dns zone create \
     -g <resource-group-name> \
-    --name 'privatelink.notebooks.azure.net'
+    --name privatelink.notebooks.azure.net
 
 az network private-dns link vnet create \
     -g <resource-group-name> \
-    --zone-name 'privatelink.notebooks.azure.net' \
+    --zone-name privatelink.notebooks.azure.net \
     --name <link-name> \
     --virtual-network <vnet-name> \
     --registration-enabled false
@@ -191,10 +178,12 @@ az network private-endpoint dns-zone-group add \
     -g <resource-group-name> \
     --endpoint-name <private-endpoint-name> \
     --name myzonegroup \
-    --private-dns-zone 'privatelink.notebooks.azure.net' \
-    --zone-name 'privatelink.notebooks.azure.net'
+    --private-dns-zone privatelink.notebooks.azure.net \
+    --zone-name privatelink.notebooks.azure.net
 ```
 
+:::zone-end
+
 ---
 
 ## Remove a private endpoint
@@ -206,24 +195,28 @@ You can remove one or all private endpoints for a hub. Removing a private endpoi
 
 To remove a private endpoint, use the following information:
 
-# [Azure portal](#tab/azure-portal)
+:::zone pivot="azure-portal"
 
 1. From the [Azure portal](https://portal.azure.com), select your hub.
 1. From the left side of the page, select __Settings__, __Networking__, and then select the __Private endpoint connections__ tab.
 1. Select the endpoint to remove and then select __Remove__.
 
     :::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.":::
 
-# [Azure CLI](#tab/cli)
+:::zone-end
+
+:::zone pivot="cli"
 
 When using the Azure CLI, use the following command to remove the private endpoint:
 
 ```azurecli
 az network private-endpoint delete \
     --name <private-endpoint-name> \
-    --resource-group <resource-group-name> \
+    --resource-group <resource-group-name>
 ```
 
+:::zone-end
+
 ---
 
 ## Enable public access
@@ -235,13 +228,15 @@ In some situations, you might want to allow someone to connect to your secured h
 
 To enable public access, use the following steps:
 
-# [Azure portal](#tab/azure-portal)
+:::zone pivot="azure-portal"
 
 1. From the [Azure portal](https://portal.azure.com), select your hub.
 1. From the left side of the page, select __Networking__ and then select the __Public access__ tab.
 1. Select __Enabled from all networks__, and then select __Save__.
 
-# [Azure CLI](#tab/cli)
+:::zone-end
+
+:::zone pivot="cli"
 
 Use the following Azure CLI command to enable public access:
 
@@ -255,8 +250,9 @@ If you receive an error that the `ml` command isn't found, use the following com
 az extension add --name ml
 ```
 
----
+:::zone-end
 
+---
 
 ## Private storage configuration
 

articles/ai-foundry/how-to/costs-plan-manage.md

@@ -111,7 +111,7 @@ Here's an example of how to monitor costs for a project. The costs are used as a
 1. Under the **Project** heading, select **Overview**. 
 1. Select **View cost for resources** from the **Total cost** section. The [Azure portal](https://portal.azure.com) opens to the resource group for your project.
 
-    :::image type="content" source="../media/cost-management/project-costs/project-settings-go-view-costs.png" alt-text="Screenshot of the Azure AI Foundry portal portal showing how to see project settings." lightbox="../media/cost-management/project-costs/project-settings-go-view-costs.png":::
+    :::image type="content" source="../media/cost-management/project-costs/project-settings-go-view-costs.png" alt-text="Screenshot of the Azure AI Foundry portal showing how to see project settings." lightbox="../media/cost-management/project-costs/project-settings-go-view-costs.png":::
 
 1. Expand the **Resource** column to see the costs for each service that's underlying your [project](../concepts/ai-resources.md#organize-work-in-projects-for-customization). But this view doesn't include costs for all resources that you use in a project.
 

articles/ai-foundry/how-to/develop/evaluate-sdk.md

@@ -233,7 +233,7 @@ import os
 from azure.identity import DefaultAzureCredential
 credential = DefaultAzureCredential()
 
-# Initialize Azure AI project and Azure OpenAI conncetion with your environment variables
+# Initialize Azure AI project and Azure OpenAI connection with your environment variables
 azure_ai_project = {
     "subscription_id": os.environ.get("AZURE_SUBSCRIPTION_ID"),
     "resource_group_name": os.environ.get("AZURE_RESOURCE_GROUP"),
@@ -250,7 +250,7 @@ model_config = {
 
 from azure.ai.evaluation import GroundednessProEvaluator, GroundednessEvaluator
 
-# Initialzing Groundedness and Groundedness Pro evaluators
+# Initializing Groundedness and Groundedness Pro evaluators
 groundedness_eval = GroundednessEvaluator(model_config)
 groundedness_pro_eval = GroundednessProEvaluator(azure_ai_project=azure_ai_project, credential=credential)
 

articles/ai-foundry/how-to/develop/run-scans-ai-red-teaming-agent.md

@@ -224,7 +224,7 @@ More advanced users can specify the desired attack strategies instead of using d
 
 Each new attack strategy specified will be applied to the set of baseline adversarial queries used in addition to the baseline adversarial queries.
 
-This following example would generate one attack objective per each of the four risk categories specified. This will first, generate four baseline adversarial prompts which would be sent to your target. Then, each baseline query would get converted into each of the four attack strategies. This will result in a total of 20 attack-response pairs from your AI system. The last attack stratgy is an example of a composition of two attack strategies to create a more complex attack query: the `AttackStrategy.Compose()` function takes in a list of two supported attack strategies and chains them together. The example's composition would first encode the baseline adversarial query into Base64 then apply the ROT13 cipher on the Base64-encoded query. Compositions only support chaining two attack strategies together.
+This following example would generate one attack objective per each of the four risk categories specified. This will first, generate four baseline adversarial prompts which would be sent to your target. Then, each baseline query would get converted into each of the four attack strategies. This will result in a total of 20 attack-response pairs from your AI system. The last attack strategy is an example of a composition of two attack strategies to create a more complex attack query: the `AttackStrategy.Compose()` function takes in a list of two supported attack strategies and chains them together. The example's composition would first encode the baseline adversarial query into Base64 then apply the ROT13 cipher on the Base64-encoded query. Compositions only support chaining two attack strategies together.
 
 ```python
 red_team_agent = RedTeam(