merge conflicts

Author: Blackmist

.openpublishing.redirection.json

@@ -1,5 +1,75 @@
 {
   "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",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/index.yml",
+      "redirect_url": "/previous-versions/azure/genomics/index",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/business-continuity-genomics.md",
+      "redirect_url": "/previous-versions/azure/genomics/business-continuity-genomics",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/file-support-ticket-genomics.md",
+      "redirect_url": "/previous-versions/azure/genomics/file-support-ticket-genomics",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/overview-what-is-genomics.md",
+      "redirect_url": "/previous-versions/azure/genomics/overview-what-is-genomics",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/quickstart-input-bam.md",
+      "redirect_url": "/previous-versions/azure/genomics/quickstart-input-bam",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/quickstart-input-multiple.md",
+      "redirect_url": "/previous-versions/azure/genomics/quickstart-input-multiple",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/quickstart-input-pair-fastq.md",
+      "redirect_url": "/previous-versions/azure/genomics/quickstart-input-pair-fastq",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/quickstart-input-sas.md",
+      "redirect_url": "/previous-versions/azure/genomics/quickstart-input-sas",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/quickstart-run-genomics-workflow-portal.md",
+      "redirect_url": "/previous-versions/azure/genomics/quickstart-run-genomics-workflow-portal",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/troubleshooting-guide-genomics.md",
+      "redirect_url": "/previous-versions/azure/genomics/troubleshooting-guide-genomics",
+      "redirect_document_id": false
+    },
+    {
+      "source_path": "articles/genomics/version-release-history-genomics.md",
+      "redirect_url": "/previous-versions/azure/genomics/version-release-history-genomics",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-studio/concepts/what-are-ai-services.md",
       "redirect_url": "/azure/ai-services/what-are-ai-services",
@@ -169,6 +239,16 @@
       "source_path_from_root": "/articles/ai-services/openai/references/azure-machine-learning.md",
       "redirect_url": "/azure/ai-services/openai/concepts/use-your-data",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/open-datasets/dataset-covid-19-open-research.md",
+      "redirect_url": "/azure/open-datasets/dataset-catalog",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/open-datasets/dataset-genomics-data-lake.md",
+      "redirect_url": "/azure/open-datasets/dataset-catalog",
+      "redirect_document_id": false
     }
   ]
-}
+}

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "cSpell.words": [
+        "DALL"
+    ]
+}

articles/ai-foundry/model-inference/how-to/configure-deployment-policies.md

@@ -15,7 +15,7 @@ ms.reviewer: fasantia
 
 # Control model deployment with custom policies
 
-When using models from Azure AI Services and Azure OpenAI with Azure AI Foundry, you might need to use custom policies to control which [type of deployment](../concepts/deployment-types.md) options are available to users or which specific models users can deploy. This article guides you on how to create policies to control model deployments using Azure Policies. 
+When using models from Azure AI Services and Azure OpenAI with [Azure AI Foundry](https://ai.azure.com), you might need to use custom policies to control which [type of deployment](../concepts/deployment-types.md) options are available to users or which specific models users can deploy. This article guides you on how to create policies to control model deployments using Azure Policies. 
 
 ## Prerequisites
 

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-model-deployments/cli.md

@@ -53,7 +53,7 @@ To add a model, you first need to identify the model that you want to deploy. Yo
 3. If you don't have an Azure AI Services account create yet, you can create one as follows:
 
     ```azurecli
-    az cognitiveservices account create -n $accountName -g $resourceGroupName
+    az cognitiveservices account create -n $accountName -g $resourceGroupName --custom-domain $accountName
     ```
 
 4. Let's see first which models are available to you and under which SKU. The following command list all the model definitions available:

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: