Merge branch 'main' into rework-how-to-fine-tune

Author: likebupt

.openpublishing.redirection.json

@@ -1,5 +1,15 @@
 {
   "redirections": [
+    {
+      "source_path": "articles/search/performance-benchmarks.md",
+      "redirect_url": "/previous-versions/azure/search/performance-benchmarks",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/search/search-traffic-analytics.md",
+      "redirect_url": "/previous-versions/azure/search/search-traffic-analytics",
+      "redirect_document_id": false
+    },
     {
       "source_path": "articles/genomics/frequently-asked-questions-genomics.yml",
       "redirect_url": "/previous-versions/azure/genomics/frequently-asked-questions-genomics",
@@ -235,6 +245,16 @@
       "redirect_url": "/azure/open-datasets/dataset-catalog",
       "redirect_document_id": false
     },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/language-studio.md",
+      "redirect_url": "/azure/ai-services/language-service/overview",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/language-service/summarization/region-support.md",
+      "redirect_url": "/azure/ai-services/language-service/concepts/regional-support",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/open-datasets/dataset-genomics-data-lake.md",
       "redirect_url": "/azure/open-datasets/dataset-catalog",

articles/ai-foundry/model-inference/faq.yml

@@ -24,18 +24,19 @@ sections:
           Both Azure OpenAI Service and Azure AI model inference are part of the Azure AI services family and build on top of the same security and enterprise promise of Azure.
 
           While Azure AI model inference focus on inference, Azure OpenAI Service can be used with more advanced APIs like batch, fine-tuning, assistants, and files.
-      - question: |
-          What's the difference between OpenAI and Azure OpenAI?
-        answer: |
-          Azure AI Models and Azure OpenAI Service give customers access to advanced language models from OpenAI with the security and enterprise promise of Azure. Azure OpenAI codevelops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.
-
-          Customers get the security capabilities of Microsoft Azure while running the same models as OpenAI. It offers private networking, regional availability, and responsible AI content filtering.  
-
-          Learn more about the [Azure OpenAI service](../../ai-services/openai/overview.md).
       - question: |
           What's the difference between Azure AI services and Azure AI Foundry?
         answer: |
           Azure AI services are a suite of AI services that provide prebuilt APIs for common AI scenarios. Azure AI Services is part of the Azure AI Foundry platform. Azure AI services can be used in Azure AI Foundry portal to enhance your models with prebuilt AI capabilities.
+      - question: |
+          What's the difference between Serverless API Endpoints and Azure AI model inference?
+        answer: |
+          Both features allow you to deploy Models-as-a-Service models in Azure AI Foundry. However, there are some differences between them:
+          - *Resource involved*: Serverless API Endpoints are deployed within an AI project resource, while Azure AI model inference is part of the Azure AI services resource.
+          - *Deployment options*: Serverless API Endpoints allow regional deployments, while Azure AI model inference allows deployments under a global capacity.
+          - *Models*: Azure AI model inference also supports deploying Azure OpenAI models.
+          - *Endpoint*: Serverless API Endpoints creates one endpoint and credential per deployment, while Azure AI model inference creates one endpoint and credential per resource.
+          - *Model router*: Azure AI model inference allows you to switch between models without changing your code using a model router.
   - name: Models
     questions:
       - question: |
@@ -44,6 +45,10 @@ sections:
           Azure AI model inference in AI services supports all the models in the Azure AI catalog having pay-as-you-go billing. For more information, see [the Models article](concepts/models.md).
 
           The Azure AI model catalog contains a wider list of models, however, those models require compute quota from your subscription. They also need to have a project or AI hub where to host the deployment. For more information, see [deployment options in Azure AI Foundry](../../ai-studio/concepts/deployments-overview.md).
+      - question: |
+          My company hasn't approved specific models for use. How can I prevent users from deploying them?
+        answer: |
+          You can restrict the models available for deployment in Azure AI services by using the Azure policies. Models are listed in the catalog but any attempt to deploy them is blocked. Read [Control AI model deployment with custom policies](how-to/configure-deployment-policies.md).
   - name: SDKs and programming languages
     questions:
       - question: |
@@ -94,10 +99,23 @@ sections:
           You can set up a spending limit in the [Azure portal](https://portal.azure.com) under **Azure Cost Management + Billing**. This limit prevents you from spending more than the limit you set. Once spending limit is reached, the subscription will be disabled and you won't be able to use the endpoint until the next billing cycle.
   - name: Data and Privacy
     questions:
+      - question: |
+          How are third-party models available? 
+        answer: |
+          Third-party models available for deployment in Azure AI Services with pay-as-you-go billing (for example, Meta AI models or Mistral models) are offered by the model provider but hosted in Microsoft-managed Azure infrastructure and accessed via API in the Azure AI model inference endpoint. Model providers define the license terms and set the price for use of their models, while Azure AI Services service manages the hosting infrastructure, makes the inference APIs available, and acts as the data processor for prompts submitted and content output by models deployed. Read about [Data privacy, and security for third-party models](../../ai-studio/how-to/concept-data-privacy.md).
+      - question: |
+          How is data processed by the Global-Standard deployment type?
+        answer: |
+          For model deployments under Azure AI Services resources, prompts and outputs are processed using Azure's global infrastructure to dynamically route traffic to the data center with best availability for each request. Global standard provides the highest default quota and eliminates the need to load balance across multiple resources. Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure location. Learn more about [data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/).
       - question: |
           Do you use my company data to train any of the models? 
         answer: |
-          Azure AI model inference doesn't use customer data to retrain models, and customer data is never shared with model providers.    
+          Azure AI model inference doesn't use customer data to retrain models, and customer data is never shared with model providers.
+      - question: |
+          Is data shared with model providers?
+        answer: |
+          Microsoft acts as the data processor for prompts and outputs sent to, and generated by, a model deployment under Azure AI services resources. Microsoft doesn't share these prompts and outputs with the model provider. Also, Microsoft doesn't use these prompts and outputs to train or improve Microsoft models, the model provider's models, or any third party's models.
+          As explained during the deployment process for Models-as-a-Service models, Microsoft might share customer contact information and transaction details (including the usage volume associated with the offering) with the model publisher so that the publisher can contact customers regarding the model. Learn more about information available to model publishers in [Access insights for the Microsoft commercial marketplace in Partner Center](/partner-center/analytics).
   - name: Customer Copyright Commitment
     questions:
       - question: |

articles/ai-foundry/model-inference/includes/configure-entra-id/intro.md

@@ -47,3 +47,17 @@ To complete this article, you need:
   * Security principal: e.g. your user account.
   * Role definition: the *Cognitive Services User* role.
   * Scope: the Azure AI Services resource.
+
+* If you want to create a custom role definition instead of using *Cognitive Services User* role, ensure the role has the following permissions:
+
+  ```json
+  {
+    "permissions": [
+      {
+        "dataActions": [
+          "Microsoft.CognitiveServices/accounts/MaaS/*"
+        ]
+      }
+    ]
+  }
+  ```

articles/ai-foundry/model-inference/includes/configure-entra-id/portal.md

@@ -14,34 +14,42 @@ zone_pivot_groups: azure-ai-models-deployment
 
 Follow these steps to configure Microsoft Entra ID for inference: 
 
-1. Go to the [Azure portal](https://portal.azure.com) and locate the Azure AI Services resource you're using. If you're using Azure AI Foundry with projects or hubs, you can navigate to it by:
+1. Go to the [Azure portal](https://portal.azure.com) and locate the **Azure AI Services** resource you're using. If you're using Azure AI Foundry with projects or hubs, you can navigate to it by:
 
    1. Go to [Azure AI Foundry portal](https://ai.azure.com).
 
    2. On the landing page, select **Open management center**.
 
    3. Go to the section **Connected resources** and select the connection to the Azure AI Services resource that you want to configure. If it isn't listed, select **View all** to see the full list.
 
+      :::image type="content" source="../../media/configure-entra-id/resource-behind-select.png" alt-text="Screenshot showing how to navigate to the details of the connection in Azure AI Foundry in the management center." lightbox="../../media/configure-entra-id/resource-behind-select.png":::
+
    4. On the **Connection details** section, under **Resource**, select the name of the Azure resource. A new page opens.
 
    5. You're now in [Azure portal](https://portal.azure.com) where you can manage all the aspects of the resource itself.
 
-2. On the left navigation bar, select **Access control (IAM)**.
+      :::image type="content" source="../../media/configure-entra-id/locate-resource-ai-services.png" alt-text="Screenshot showing the resource to which we configure Microsoft Entra ID." lightbox="../../media/configure-entra-id/locate-resource-ai-services.png":::
+
+2. On the left navigation bar, select **Access control (IAM)** and then select **Add** > **Add role assignment**.
+
+   :::image type="content" source="../../media/configure-entra-id/resource-aim.png" alt-text="Screenshot showing how to add a role assignment in the Access control section of the resource in the Azure portal." lightbox="../../media/configure-entra-id/resource-aim.png":::
 
    > [!TIP]
    > Use the **View my access** option to verify which roles are already assigned to you.
 
-3. Select **Role assignments** and then select **Add** > **Add role assignment**.
+3. On **Job function roles**, type **Cognitive Services User**. The list of roles is filtered out.
 
-4. On **Job function roles**, type **Cognitive Services User**. The list of roles is filtered out.
+   :::image type="content" source="../../media/configure-entra-id/cognitive-services-user.png" alt-text="Screenshot showing how to select the Cognitive Services User role assignment." lightbox="../../media/configure-entra-id/cognitive-services-user.png":::
 
-5.  Select the role and select **Next**.
+4. Select the role and select **Next**.
 
-6.  On **Members**, select the user or group you want to grant access to. We recommend using security groups whenever possible as they are easier to manage and maintain. 
+5. On **Members**, select the user or group you want to grant access to. We recommend using security groups whenever possible as they are easier to manage and maintain. 
 
-7.  Select **Next** and finish the wizard.
+   :::image type="content" source="../../media/configure-entra-id/select-user.png" alt-text="Screenshot showing how to select the user to whom assign the role." lightbox="../../media/configure-entra-id/select-user.png":::
 
-8.  The selected user can now use Microsoft Entra ID for inference.
+6. Select **Next** and finish the wizard.
+
+7. The selected user can now use Microsoft Entra ID for inference.
 
     > [!TIP]
     > Keep in mind that Azure role assignments may take up to five minutes to propagate. When working with security groups, adding or removing users from the security group propagates immediately.
@@ -84,6 +92,4 @@ To change this behavior, you have to update the connections from your projects t
 
 ## Disable key-based authentication in the resource
 
-Disabling key-based authentication is advisable when you implemented Microsoft Entra ID and fully addressed compatibility or fallback concerns in all the applications that consume the service.
-
-
+Disabling key-based authentication is advisable when you implemented Microsoft Entra ID and fully addressed compatibility or fallback concerns in all the applications that consume the service. Disabling key-based authentication is only available when deploying using Bicep/ARM.

articles/ai-foundry/model-inference/includes/configure-entra-id/troubleshooting.md

@@ -7,6 +7,21 @@ ms.date: 01/23/2025
 ms.topic: include
 ---
 
+Before troubleshooting, verify that you have the right permissions assigned:
+
+1. Go to the [Azure portal](https://portal.azure.com) and locate the **Azure AI Services** resource you're using.
+
+2. On the left navigation bar, select **Access control (IAM)** and then select **Check access**.
+
+3. Type the name of the user or identity you are using to connect to the service.
+
+4. Verify that the role **Cognitive Services User** is listed (or a role that contains the required permissions as explained in [Prerequisites](#prerequisites)).
+
+    > [!IMPORTANT]
+    > Roles like **Owner** or **Contributor** don't provide access via Microsoft Entra ID.
+
+5. If not listed, follow the steps in this guide before continuing.
+
 The following table contains multiple scenarios that can help troubleshooting Microsoft Entra ID:
 
 | Error / Scenario     | Root cause    | Solution |

articles/ai-foundry/model-inference/includes/create-resources/bicep.md

@@ -30,19 +30,9 @@ The files for this example are in:
 cd azureai-model-inference-bicep/infra
 ```
 
-## Understand the resources
-
-The tutorial helps you create:
-
-> [!div class="checklist"]
-> * An Azure AI Services resource.
-> * A model deployment in the Global standard SKU for each of the models supporting pay-as-you-go.
-> * (Optionally) An Azure AI project and hub.
-> * (Optionally) A connection between the hub and the models in Azure AI Services.
-
-Notice that **you have to deploy an Azure AI project and hub** if you plan to use the Azure AI Foundry portal for managing the resource, using playground, or any other feature from the portal.
+## Create the resources
 
-You are using the following assets to create those resources:
+Follow these steps:
 
 1. Use the template `modules/ai-services-template.bicep` to describe your Azure AI Services resource:
 
@@ -72,10 +62,6 @@ You are using the following assets to create those resources:
 
     :::code language="bicep" source="~/azureai-model-inference-bicep/infra/modules/ai-services-connection-template.bicep":::
 
-## Create the resources
-
-In your console, follow these steps:
-
 1. Define the main deployment:
 
     __deploy-with-project.bicep__

articles/ai-foundry/model-inference/includes/create-resources/intro.md

@@ -11,6 +11,22 @@ ms.topic: include
 
 In this article, you learn how to create the resources required to use Azure AI model inference and consume flagship models from Azure AI model catalog.
 
+## Understand the resources
+
+Azure AI model inference is a capability in Azure AI Services resources in Azure. You can create model deployments under the resource to consume their predictions. You can also connect the resource to Azure AI Hubs and Projects in Azure AI Foundry to create intelligent applications if needed. The following picture shows the high level architecture.
+
+:::image type="content" source="../../media/create-resources/resources-architecture.png" alt-text="A diagram showing the high level architecture of the resources created in the tutorial." lightbox="../../media/create-resources/resources-architecture.png":::
+
+Azure AI Services resources don't require AI projects or AI hubs to operate and you can create them to consume flagship models from your applications. However, additional capabilities are available if you **deploy an Azure AI project and hub**, including playground, or agents.
+
+The tutorial helps you create:
+
+> [!div class="checklist"]
+> * An Azure AI Services resource.
+> * A model deployment for each of the models supported with pay-as-you-go.
+> * (Optionally) An Azure AI project and hub.
+> * (Optionally) A connection between the hub and the models in Azure AI Services.
+
 ## Prerequisites
 
 To complete this article, you need: