Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into managing-payment-methods

Author: ShawnJackson

.openpublishing.publish.config.json

@@ -1289,7 +1289,6 @@
   ".openpublishing.redirection.azure-databricks.json",
   ".openpublishing.redirection.azure-datalake-storage-gen1.json",
   ".openpublishing.redirection.azure-hpc.json",
-  ".openpublishing.redirection.azure-kubernetes-service.json",
   ".openpublishing.redirection.azure-monitor.json",
   ".openpublishing.redirection.azure-percept.json",
   ".openpublishing.redirection.azure-productivity.json",
@@ -1337,7 +1336,6 @@
   "articles/active-directory-b2c/.openpublishing.redirection.active-directory-b2c.json",
   "articles/ai-services.openpublishing.redirection.ai-services.json",
   "articles/ai-studio/.openpublishing.redirection.ai-studio.json",
-  "articles/aks/.openpublishing.redirection.aks.json",
   "articles/analysis-services/.openpublishing.redirection.analysis-services.json",
   "articles/application-gateway/.openpublishing.redirection.application-gateway.json",
   "articles/automation/.openpublishing.redirection.automation.json",

.openpublishing.redirection.azure-kubernetes-service.json

@@ -165,6 +165,16 @@
             "source_path": "articles/container-registry/monitor-service-reference.md",
             "redirect_url": "/azure/container-registry/monitor-container-registry-reference",
             "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/container-registry/container-registry-auth-aks.md",
+            "redirect_url": "/azure/aks/cluster-container-registry-integration",
+            "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/container-service/kubernetes/container-service-kubernetes-jenkins.md",
+            "redirect_url": "/azure/aks/jenkins-continuous-deployment",
+            "redirect_document_id": false
         }
     ]
 }

.openpublishing.redirection.container-registry.json

@@ -42,7 +42,7 @@ As a *Global Administrator* in an Azure AD B2C tenant, you can restrict non-admi
 
 1. Under **Manage**, select **User Settings**.
 
-1. Under **Tenant creation**, select **Yes**. 
+1. Under **Default user role permissions**, for **Restrict non-admin users from creating tenants**, select **Yes**. 
 
 1. At the top of the **User Settings** page, select **Save**. 
 
@@ -58,7 +58,7 @@ Before you create an Azure AD B2C tenant, make sure that you've the permission t
 
 1. Under **Manage**, select **User Settings**.
 
-1. Review your **Tenant Creation** setting. If the settings is set to **No**, then contact your administrator to assign the tenant creator role to you. The setting is greyed out if you're not an administrator in the tenant.
+1.  Under **Default user role permissions**, review your **Restrict non-admin users from creating tenants** setting. If the setting is set to **No**, then contact your administrator to assign the tenant creator role to you. The setting is greyed out if you're not an administrator in the tenant.
 
 
 ## Next steps 

.openpublishing.redirection.json

@@ -27,7 +27,7 @@ The following section walks through a sample image moderation request with cURL.
 
 Choose a sample image to analyze, and download it to your device. 
 
-We support JPEG, PNG, GIF, BMP, TIFF, or WEBP image formats. The maximum size for image submissions is 4 MB, and image dimensions must be between 50 x 50 pixels and 2,048 x 2,048 pixels. If your format is animated, we'll extract the first frame to do the detection.
+We support JPEG, PNG, GIF, BMP, TIFF, or WEBP image formats. The maximum size for image submissions is 4 MB, and image dimensions must be between 50 x 50 pixels and 7,200 x 7,200 pixels. If your format is animated, we'll extract the first frame to do the detection.
 
 You can input your image by one of two methods: **local filestream** or **blob storage URL**.
 - **Local filestream** (recommended): Encode your image to base64. You can use a website like [codebeautify](https://codebeautify.org/image-to-base64-converter) to do the encoding. Then save the encoded string to a temporary location. 
@@ -79,7 +79,7 @@ The parameters in the request body are defined in this table:
 
 | Name        | Required?     | Description  | Type    |
 | :---------- | ----------- | :------------ | ------- |
-| **content**    | Required | 	The content or blob URL of the image. I can be either base64-encoded bytes or a blob URL. If both are given, the request is refused. The maximum allowed size of the image is 2048 pixels x 2048 pixels, and the maximum file size is 4 MB. The minimum size of the image is 50 pixels x 50 pixels. | String  |
+| **content**    | Required | 	The content or blob URL of the image. I can be either base64-encoded bytes or a blob URL. If both are given, the request is refused. The maximum allowed size of the image is 7,200 x 7,200 pixels, and the maximum file size is 4 MB. The minimum size of the image is 50 pixels x 50 pixels. | String  |
 | **categories** | Optional | This is assumed to be an array of category names. See the [Harm categories guide](../../concepts/harm-categories.md) for a list of available category names. If no categories are specified, all four categories are used. We use multiple categories to get scores in a single request. | String  |
 | **outputType** | Optional | Image moderation API only supports `"FourSeverityLevels"`. Output severities in four levels. The value can be `0,2,4,6` | String|
 

articles/active-directory-b2c/tenant-management-check-tenant-creation-permission.md

@@ -54,7 +54,7 @@ To create a custom extraction model, label a dataset of documents with the value
 > Starting with version 4.0 (2024-02-29-preview) API, custom neural models now support **overlapping fields** and **table, row and cell level confidence**.
 >
 
-The custom neural (custom document) model uses deep learning models and  base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support structured, semi-structured, and unstructured documents to extract fields. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
+The custom neural (custom document) model uses deep learning models and  base model trained on a large collection of documents. This model is then fine-tuned or adapted to your data when you train the model with a labeled dataset. Custom neural models support extracting key data fields from structured, semi-structured, and unstructured documents. When you're choosing between the two model types, start with a neural model to determine if it meets your functional needs. See [neural models](concept-custom-neural.md) to learn more about custom document models.
 
 ### Custom template model
 

articles/ai-services/content-safety/includes/quickstarts/rest-quickstart-image.md

@@ -349,18 +349,20 @@ You can deploy to a standalone Teams app directly from Azure OpenAI Studio. Foll
 
 1. Provision your app: (detailed instructions in [Provision cloud resources](/microsoftteams/platform/toolkit/provision))
 
-1. Assign the **Cognitive Service OpenAI User** role to your deployed App Service resource 
-    1. Go to the Azure portal and select the newly created Azure App Service resource
-    1. Go to **settings** -> **identity** -> **enable system assigned identity** 
-        1. Select **Azure role assignments** and then **add role assignments**. Specify the following parameters:
-           * Scope: resource group 
-           * Subscription: the subscription of your Azure OpenAI resource 
-           * Resource group of your Azure OpenAI resource 
-           * Role: **Cognitive Service OpenAI user**  
+1. Assign the **Cognitive Service OpenAI User** role to your deployed **User Assigned Managed Identity** resource of your custom copilot. 
+    1. Go to the Azure portal and select the newly created **User Assigned Managed Identity** resource for your custom copilot.
+    1. Go to **Azure Role Assignments**.
+    1. Select **add role assignment**. Specify the following parameters:
+        * Scope: resource group 
+        * Subscription: the subscription of your Azure OpenAI resource 
+        * Resource group of your Azure OpenAI resource 
+        * Role: **Cognitive Service OpenAI user**  
 
 1. Deploy your app to Azure by following the instructions in [Deploy to the cloud](/microsoftteams/platform/toolkit/deploy). 
 
 1. Publish your app to Teams by following the instructions in [Publish Teams app](/microsoftteams/platform/toolkit/publish).
+    > [!IMPORTANT]
+    > Your Teams app is intended for use within the same tenant of your Azure account used during setup, as it is securely configured by default for single-tenant usage. Using this app with a Teams account not associated with the Azure tenant used during setup will result in an error.
 
 The README file in your Teams app has additional details and tips. Also, see [Tutorial - Build Custom Copilot using Teams](/microsoftteams/platform/teams-ai-library-tutorial) for guided steps.
 

articles/ai-services/document-intelligence/concept-custom.md

@@ -7,6 +7,12 @@ ms.author: eur
 ms.custom: references_regions
 ---
 
+### July 2024 release
+
+#### Text to speech avatar (GA)
+
+Text to speech avatar is now generally available. For more information, see [text to speech avatar](../../text-to-speech-avatar/what-is-text-to-speech-avatar.md).
+
 ### June 2024 release
 
 #### Prebuilt neural voice

articles/ai-services/openai/concepts/use-your-data.md

@@ -15,17 +15,14 @@ ms.custom: references_regions
 
 # Synthetic data generation in Azure AI Studio
 
-In this article
-  - [Synthetic data generation](#synthetic-data-generation)
-  - [Next Steps](#next-steps)
+In Azure AI Studio, you can use synthetic data generation to efficiently produce predictions for your datasets. In this article, you're introduced to the concept of synthetic data generation and how it can be used in machine learning.
 
-In Azure AI Studio, you can leverage synthetic data generation to efficiently produce predictions for your datasets.
 
 ## Synthetic data generation
 
 Synthetic data generation involves creating artificial data that mimics the statistical properties of real-world data. This data is generated using algorithms and machine learning techniques, and it can be used in various ways, such as computer simulations or by modeling real-world events.
 
-In machine learning, synthetic data is particularly valuable for several reasons:
+In machine learning, synthetic data is valuable for several reasons:
 
 **Data Augmentation:** It helps in expanding the size of training datasets, which is crucial for training robust machine learning models. This is especially useful when real-world data is scarce or expensive to obtain.
 
@@ -37,4 +34,4 @@ You can use the sample notebook available at this [link](https://aka.ms/meta-lla
 - [What is Azure AI Studio?](../what-is-ai-studio.md)
 - [Learn more about deploying Meta Llama models](../how-to/deploy-models-llama.md)
 
-- [Azure AI FAQ article](../faq.yml)
+- [Azure AI FAQ article](../faq.yml)