Merge branch 'MicrosoftDocs:main' into shbatr/funcscenarios

Author: shreyabatra4

.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

@@ -1,7 +1,7 @@
 ---
 title: Deploy custom language projects to multiple regions in Azure AI Language
 titleSuffix: Azure AI services
-description: Learn about deploying your language projects to multiple regions.
+description: Learn about how to deploy your custom language projects to multiple regions.
 #services: cognitive-services
 author: jboback
 manager: nitinme
@@ -16,54 +16,57 @@ ms.custom: language-service-clu
 
 > [!NOTE]
 > This article applies to the following custom features in Azure AI Language:
+>
 > * [Conversational language understanding](../../conversational-language-understanding/overview.md)
 > * [Custom text classification](../../custom-text-classification/overview.md)
-> * [Custom NER](../../custom-named-entity-recognition/overview.md)
+> * [Custom named entity recognition (NER)](../../custom-named-entity-recognition/overview.md)
 > * [Orchestration workflow](../../orchestration-workflow/overview.md)
 
-Custom Language service features enable you to deploy your project to more than one region, making it much easier to access your project globally while managing only one instance of your project in one place. 
+Custom language service features enable you to deploy your project to more than one region. This capability makes it much easier to access your project globally while you manage only one instance of your project in one place.
 
-Before you deploy a project, you can assign **deployment resources** in other regions. Each deployment resource is a different Language resource from the one you use to author your project. You deploy to those resources and then target your prediction requests to that resource in their respective regions and your queries are served directly from that region.
+Before you deploy a project, you can assign *deployment resources* in other regions. Each deployment resource is a different Language resource from the one that you use to author your project. You deploy to those resources and then target your prediction requests to that resource in their respective regions and your queries are served directly from that region.
 
-When creating a deployment, you can select which of your assigned deployment resources and their corresponding regions you would like to deploy to. The model you deploy is then replicated to each region and accessible with its own endpoint dependent on the deployment resource's custom subdomain. 
+When you create a deployment, you can select which of your assigned deployment resources and their corresponding regions you want to deploy to. The model you deploy is then replicated to each region and accessible with its own endpoint dependent on the deployment resource's custom subdomain.
 
 ## Example
 
-Suppose you want to make sure your project, which is used as part of a customer support chatbot, is accessible by customers across the US and India. You would author a project with the name **ContosoSupport** using a _West US 2_ Language resource named **MyWestUS2**. Before deployment, you would assign two deployment resources to your project - **MyEastUS** and **MyCentralIndia** in _East US_ and _Central India_, respectively.
+Suppose you want to make sure your project, which is used as part of a customer support chatbot, is accessible by customers across the United States and India. You author a project with the name `ContosoSupport` by using a West US 2 Language resource named `MyWestUS2`. Before deployment, you assign two deployment resources to your project: `MyEastUS` and `MyCentralIndia` in East US and Central India, respectively.
+
+When you deploy your project, you select all three regions for deployment: the original West US 2 region and the assigned ones through East US and Central India.
 
-When deploying your project, You would select all three regions for deployment: the original _West US 2_ region and the assigned ones through _East US_ and _Central India_.
+You now have three different endpoint URLs to access your project in all three regions:
 
-You would now have three different endpoint URLs to access your project in all three regions:
-* West US 2: `https://mywestus2.cognitiveservices.azure.com/language/:analyze-conversations`
-* East US: `https://myeastus.cognitiveservices.azure.com/language/:analyze-conversations`
-* Central India: `https://mycentralindia.cognitiveservices.azure.com/language/:analyze-conversations`
+* **West US 2**: `https://mywestus2.cognitiveservices.azure.com/language/:analyze-conversations`
+* **East US**: `https://myeastus.cognitiveservices.azure.com/language/:analyze-conversations`
+* **Central India**: `https://mycentralindia.cognitiveservices.azure.com/language/:analyze-conversations`
 
-The same request body to each of those different URLs serves the exact same response directly from that region. 
+The same request body to each of those different URLs serves the exact same response directly from that region.
 
 ## Validations and requirements
 
-Assigning deployment resources requires Microsoft Entra authentication. Microsoft Entra ID is used to confirm you have access to the resources you are interested in assigning to your project for multi-region deployment. In the Language Studio, you can automatically [enable Microsoft Entra authentication](https://aka.ms/rbac-language) by assigning yourself the _Cognitive Services Language Owner_ role to your original resource. To programmatically use Microsoft Entra authentication, learn more from the [Azure AI services documentation](../../../authentication.md?source=docs&tabs=powershell&tryIt=true#authenticate-with-azure-active-directory).
+Assigning deployment resources requires Microsoft Entra authentication. Microsoft Entra ID is used to confirm that you have access to the resources that you want to assign to your project for multiregion deployment. In Language Studio, you can automatically [enable Microsoft Entra authentication](https://aka.ms/rbac-language) by assigning yourself the Azure Cognitive Services Language Owner role to your original resource. To programmatically use Microsoft Entra authentication, learn more from the [Azure AI services documentation](../../../authentication.md?source=docs&tabs=powershell&tryIt=true#authenticate-with-azure-active-directory).
 
-Your project name and resource are used as its main identifiers. Therefore, a Language resource can only have a specific project name in each resource. Any other projects with the same name will not be deployable to that resource. 
+Your project name and resource are used as its main identifiers. A Language resource can only have a specific project name in each resource. Any other projects with the same name can't be deployed to that resource.
 
-For example, if a project **ContosoSupport** was created by resource **MyWestUS2** in _West US 2_ and deployed to resource **MyEastUS** in _East US_, the resource **MyEastUS** cannot create a different project called **ContosoSupport** and deploy a project to that region. Similarly, your collaborators cannot then create a project **ContosoSupport** with resource **MyCentralIndia** in _Central India_ and deploy it to either **MyWestUS2** or **MyEastUS**.
+For example, if a project `ContosoSupport` was created by the resource `MyWestUS2` in West US 2 and deployed to the resource `MyEastUS` in East US, the resource `MyEastUS` can't create a different project called `ContosoSupport` and deploy a project to that region. Similarly, your collaborators can't then create a project `ContosoSupport` with the resource `MyCentralIndia` in Central India and deploy it to either `MyWestUS2` or `MyEastUS`.
 
-You can only swap deployments that are available in the exact same regions, otherwise swapping will fail.
+You can only swap deployments that are available in the exact same regions. Otherwise, swapping fails.
 
-If you remove an assigned resource from your project, all of the project deployments to that resource will then be deleted.
+If you remove an assigned resource from your project, all of the project deployments to that resource are deleted.
 
 > [!NOTE]
 > Orchestration workflow only:
 >
-> You **cannot** assign deployment resources to orchestration workflow projects with custom question answering or LUIS connections. You subsequently cannot add custom question answering or LUIS connections to projects that have assigned resources.
+> You *can't* assign deployment resources to orchestration workflow projects with custom question answering or LUIS connections. Subsequently, you can't add custom question answering or LUIS connections to projects that have assigned resources.
 >
-> For multi-region deployment to work as expected, the connected CLU projects **must also be deployed** to the same regional resources you've deployed the orchestration workflow project to. Otherwise the orchestration workflow project will attempt to route a request to a deployment in its region that doesn't exist.
+> For multiregion deployment to work as expected, the connected CLU projects *must also be deployed* to the same regional resources to which you deployed the orchestration workflow project. Otherwise, the orchestration workflow project attempts to route a request to a deployment in its region that doesn't exist.
 
 Some regions are only available for deployment and not for authoring projects.
 
-## Next steps
+## Related content
 
 Learn how to deploy models for:
+
 * [Conversational language understanding](../../conversational-language-understanding/how-to/deploy-model.md)
 * [Custom text classification](../../custom-text-classification/how-to/deploy-model.md)
 * [Custom NER](../../custom-named-entity-recognition/how-to/deploy-model.md)