@@ -12,7 +12,10 @@ ms.author: jboback
 ms.custom: language-service-custom-ta4h
 ---
 
-# What is custom Text Analytics for health? 
+# What is custom Text Analytics for health?
+
+> [!NOTE]
+> Custom text analytics for health (preview) will be retired on 10 January 2025, please transition to other custom model training services, such as custom named entity recognition in Azure AI Language, by that date. From now to 10 January 2025, you can continue to use custom text analytics for health (preview) in your existing projects without disruption. You can’t create new projects. On 10 January 2025 – workloads running on custom text analytics for health (preview) will be deleted and associated project data will be lost.
 
 Custom Text Analytics for health is one of the custom features offered by [Azure AI Language](../overview.md). It is a cloud-based API service that applies machine-learning intelligence to enable you to build custom models on top of [Text Analytics for health](../text-analytics-for-health/overview.md) for custom healthcare entity recognition tasks.
 

@@ -15,6 +15,9 @@ zone_pivot_groups: usage-custom-language-features
 
 # Quickstart: custom Text Analytics for health
 
+> [!NOTE]
+> Custom text analytics for health (preview) will be retired on 10 January 2025, please transition to other custom model training services, such as custom named entity recognition in Azure AI Language, by that date. From now to 10 January 2025, you can continue to use custom text analytics for health (preview) in your existing projects without disruption. You can’t create new projects. On 10 January 2025 – workloads running on custom text analytics for health (preview) will be deleted and associated project data will be lost.
+
 Use this article to get started with creating a custom Text Analytics for health project where you can train custom models on top of Text Analytics for health for custom entity recognition. A model is artificial intelligence software that's trained to do a certain task. For this system, the models extract healthcare related named entities and are trained by learning from labeled data.
 
 In this article, we use Language Studio to demonstrate key concepts of custom Text Analytics for health. As an example we’ll build a custom Text Analytics for health model to extract the Facility or treatment location from short discharge notes.

@@ -1,76 +0,0 @@
----
-title: Connect custom question answering with Azure OpenAI on your data 
-titleSuffix: Azure AI services
-description: Learn how to use custom question answering with Azure OpenAI.
-ms.service: azure-ai-language
-author: jboback
-ms.author: jboback
-ms.topic: how-to
-ms.date: 02/09/2024
----
-
-# Connect custom question answering with Azure OpenAI on your data 
-
-Custom question answering enables you to create a conversational layer on your data based on sophisticated Natural Language Processing (NLP) capabilities with enhanced relevance using a deep learning ranker, precise answers, and end-to-end region support. Most use cases for custom question answering rely on finding appropriate answers for inputs by integrating it with chat bots, social media applications and speech-enabled desktop applications. 
-
-AI runtimes however, are evolving due to the development of Large Language Models (LLMs), such as GPT-35-Turbo and GPT-4 offered by [Azure OpenAI](../../../openai/overview.md) can address many chat-based use cases, which you may want to integrate with.
-
-At the same time, customers often require a custom answer authoring experience to achieve more granular control over the quality and content of question-answer pairs, and allow them to address content issues in production. Read this article to learn how to integrate Azure OpenAI On Your Data (Preview) with question-answer pairs from your custom question answering project, using your project's underlying Azure AI Search indexes.
-
-## Prerequisites
-
-* An existing Azure OpenAI resource. If you don't already have an Azure OpenAI resource, then [create one and deploy a model](../../../openai/how-to/create-resource.md).
-* An Azure Language Service resource and custom question answering project. If you don’t have one already, then [create one](../quickstart/sdk.md). 
-    * Be sure that you are assigned at least the [Cognitive Services OpenAI Contributor role](/azure/role-based-access-control/built-in-roles#cognitive-services-openai-contributor) for the Azure OpenAI resource.
-
-
-## Connect Azure OpenAI on your data and custom question answering
-
-1.	Sign in to [Language Studio](https://aka.ms/languageStudio) and navigate to your custom question answering project with an existing deployment.
-
-    :::image type="content" source="../media/question-answering/deployment-sources.png" alt-text="A screenshot showing a custom question answering deployment." lightbox="../media/question-answering/deployment-sources.png":::
-
-1. Select the **Azure Search** tab on the navigation menu to the left.
-
-1. Make a note of your Azure Search details, such as Azure Search resource name, subscription, and location. You will need this information when you connect your Azure AI Search index to Azure OpenAI.
-
-    :::image type="content" source="../media/question-answering/azure-search.png" alt-text="A screenshot showing the Azure search section for a custom question answering project." lightbox="../media/question-answering/azure-search.png":::
-
-1. Navigate to [Azure OpenAI Studio](https://oai.azure.com/) and sign-in with credentials that have access to your Azure OpenAI resource.
-
-1. Select the **Bring your own data** tile to start connecting your search index. You can also select the **Chat playground** tile.
-
-    :::image type="content" source="../../../openai/media/quickstarts/chat-playground-card.png" alt-text="A screenshot of the Azure OpenAI Studio landing page." lightbox="../../../openai/media/quickstarts/chat-playground-card.png":::
-
-    And on the **Assistant setup** tile, select **Add your data (preview)** > **+ Add a data source**.
-
-    :::image type="content" source="../../../openai/media/quickstarts/chatgpt-playground-add-your-data.png" alt-text="A screenshot showing the button for adding your data in Azure OpenAI Studio." lightbox="../../../openai/media/quickstarts/chatgpt-playground-add-your-data.png":::
-
-1. In the pane that appears, select **Azure AI Search** under **Select or add data source**. This will update the screen with **Data field mapping** options depending on your data source.
-        
-    :::image type="content" source="../media/question-answering/data-source-selection.png" alt-text="A screenshot showing data selection options in Azure OpenAI Studio." lightbox="../media/question-answering/data-source-selection.png":::
-                    
-1. Select the subscription, Azure AI Search service and Azure AI Search Index associated with your custom question Answering project. Select the acknowledgment that connecting it will incur usage on your account. Then select **Next**.
-
-    :::image type="content" source="../media/question-answering/azure-search-data-source.png" alt-text="A screenshot showing selection information for Azure AI Search in Azure OpenAI Studio." lightbox="../media/question-answering/azure-search-data-source.png":::
-
-1. On the **Index data field mapping** screen, select *answer* for **Content data** field. The other fields such as **File name**, **Title** and **URL** are optional depending on the nature of your data source.
-
-    :::image type="content" source="../media/question-answering/data-field-mapping.png" alt-text="A screenshot showing index field mapping information for Azure AI Search in Azure OpenAI Studio." lightbox="../media/question-answering/data-field-mapping.png":::
-
-1. Select **Next**. Select a search type from the dropdown menu. You can choose **Keyword** or **Semantic**. semantic” search requires an existing semantic search configuration which may or may not be available for your project.  
-    
-    :::image type="content" source="../media/question-answering/data-management.png" alt-text="A screenshot showing the data management options for Azure AI Search indexes." lightbox="../media/question-answering/data-management.png":::
-    
-1. Review the information you provided, and select **Save and close**. 
-
-    :::image type="content" source="../media/question-answering/confirm-details.png" alt-text="A screenshot showing the confirmation screen." lightbox="../media/question-answering/confirm-details.png":::
-
-1. Your data source has now been added. Select your model's deployment name under the **Configuration** > **Deployment** tab on the menu to the right. 
-
-    :::image type="content" source="../media/question-answering/chat-playground.png" alt-text="A screenshot of the playground page of the Azure OpenAI Studio with sections highlighted." lightbox="../media/question-answering/chat-playground.png":::
-
-You can now start exploring Azure OpenAI capabilities with a no-code approach through the chat playground. It's simply a text box where you can submit a prompt to generate a completion. From this page, you can quickly iterate and experiment with the capabilities. You can also launch a [web app](../../../openai/how-to/use-web-app.md) to chat with the model over the web.
-
-## Next steps
-* [Using Azure OpenAI on your data](../../../openai/concepts/use-your-data.md)
\ No newline at end of file

@@ -11,9 +11,6 @@ ms.custom: language-service-question-answering
 
 # Migrate from QnA Maker knowledge bases to custom question answering
 
-> [!NOTE]
-> You can also migrate to [Azure OpenAI](../../../qnamaker/How-To/migrate-to-openai.md).
-
 Custom question answering, a feature of Azure AI Language was introduced in May 2021 with several new capabilities including enhanced relevance using a deep learning ranker, precise answers, and end-to-end region support. Each custom question answering project is equivalent to a knowledge base in QnA Maker. You can easily migrate knowledge bases from a QnA Maker resource to custom question answering projects within a [language resource](https://aka.ms/create-language-resource). You can also choose to migrate knowledge bases from multiple QnA Maker resources to a specific language resource.
 
 To successfully migrate knowledge bases, **the account performing the migration needs contributor access to the selected QnA Maker and language resource**. When a knowledge base is migrated, the following details are copied to the new custom question answering project:

@@ -13,9 +13,6 @@ ms.custom: language-service-question-answering
 
 # What is custom question answering?
 
-> [!NOTE]
-> [Azure OpenAI On Your Data](../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure OpenAI On Your Data, please check out our [guide]( how-to/azure-openai-integration.md).
-
 Custom question answering provides cloud-based Natural Language Processing (NLP) that allows you to create a natural conversational layer over your data. It is used to find appropriate answers from customer input or from a project.
 
 Custom question answering is commonly used to build conversational client applications, which include social media applications, chat bots, and speech-enabled desktop applications. This offering includes features like enhanced relevance using a deep learning ranker, precise answers, and end-to-end region support.

@@ -15,9 +15,6 @@ zone_pivot_groups: custom-qna-quickstart
 
 # Quickstart: custom question answering
 
-> [!NOTE]
-> [Azure OpenAI On Your Data](../../../openai/concepts/use-your-data.md) utilizes large language models (LLMs) to produce similar results to Custom Question Answering. If you wish to connect an existing Custom Question Answering project to Azure OpenAI On Your Data, please check out our [guide](../how-to/azure-openai-integration.md).
-
 > [!NOTE]
 > Are you looking to migrate your workloads from QnA Maker? See our [migration guide](../how-to/migrate-qnamaker-to-question-answering.md) for information on feature comparisons and migration steps.
 

@@ -15,6 +15,9 @@ zone_pivot_groups: usage-custom-language-features
 
 # Quickstart: Custom sentiment analysis (preview)
 
+> [NOTE]
+> Custom sentiment analysis (preview) will be retired on 10 January 2025, please transition to other custom model training services, such as custom text classification in Azure AI Language, by that date. From now to 10 January 2025, you can continue to use custom sentiment analysis (preview) in your existing projects without disruption. You can’t create new projects. On 10 January 2025 – workloads running on custom sentiment analysis (preview) will be deleted and associated project data will be lost. 
+
 Use this article to get started with creating a Custom sentiment analysis project where you can train custom models for detecting the sentiment of text. A model is artificial intelligence software that's trained to do a certain task. For this system, the models classify text, and are trained by learning from tagged data.
 
 ::: zone pivot="language-studio"

@@ -35,7 +35,7 @@ Ensure that the AI Studio hub is deployed with the __Identity-based access__ set
 
 - In the Azure portal, select the hub and then select __Settings__, __Properties__, and __Options__. At the bottom of the page, verify that __Storage account access type__ is set to __Identity-based access__.
 - If deploying using Azure Resource Manager or Bicep templates, include the `systemDatastoresAuthMode: 'identity'` property in your deployment template.
-
+- You must be familiar with using Microsoft Entra ID role-based access control to assign roles to resources and users. For more information, visit the [Role-based access control](/azure/role-based-access-control/overview) article.
 
 ## Configure Network Isolated AI Studio Hub
 
@@ -179,6 +179,8 @@ Repeat these steps for each resource that you want to connect to using Microsoft
 
 The services need to authorize each other to access the connected resources. The admin performing the configuration needs to have the __Owner__ role on these resources to add role assignments. The following table lists the required role assignments for each resource. The __Assignee__ column refers to the system-assigned managed identity of the listed resource. The __Resource__ column refers to the resource that the assignee needs to access. For example, the Azure AI Search has a system-assigned managed identity that needs to be assigned the __Storage Blob Data Contributor__ role for the Azure Storage Account.
 
+For more information on assigning roles, see [Tutorial: Grant a user access to resources](/azure/role-based-access-control/quickstart-assign-role-user-portal).
+
 | Resource | Role | Assignee | Description |
 |----------|------|----------|-------------|
 | Azure AI Search | Search Index Data Contributor | Azure AI services/OpenAI | Read-write access to content in indexes. Import, refresh, or query the documents collection of an index. Only used for ingestion and inference scenarios. |
@@ -192,19 +194,23 @@ The services need to authorize each other to access the connected resources. The
 > [!NOTE]
 > The Cognitive Services OpenAI User role is only required if you are using two Azure OpenAI resources: one for your chat model and one for your embedding model. If this applies, enable Trusted Services AND ensure the Connection for your embedding model Azure OpenAI resource has EntraID enabled.  
 
-To enable your developers to use these resources to build applications, add the developers' identity with the following role assignments to the listed resources.
-
-| Resource | Role | Description |
-|----------|------|-------------|
-| Azure AI Search | Contributor | List API-Keys to list indexes from Azure OpenAI Studio. |
-| Azure AI Search | Search Index Data Contributor | Required for the indexing scenario. |
-| Azure AI services/OpenAI | Cognitive Services OpenAI Contributor | Call public ingestion API from Azure OpenAI Studio. |
-| Azure AI services/OpenAI | Cognitive Services User | List API-Keys from Azure OpenAI Studio. |
-| Azure AI services/OpenAI | Contributor | Allows for calls to the control plane. |
-| Azure Storage Account | Contributor | List Account SAS to upload files from Azure OpenAI Studio. |
-| Azure Storage Account | Storage Blob Data Contributor | Needed for developers to read and write to blob storage. |
-| Azure Storage Account | Storage File Data Privileged Contributor | Needed to Access File Share in Storage for Promptflow data. |
-| The resource group or Azure subscription where the developer need to deploy the web app to | Contributor | Deploy web app to the developer's Azure subscription. |
+### Assign roles to developers
+
+To enable your developers to use these resources to build applications, assign the following roles to your developer's identity in Microsoft Entra ID. For example, assign the __Search Services Contributor__ role to the developer's Microsoft Entra ID for the Azure AI Search resource.
+
+For more information on assigning roles, see [Tutorial: Grant a user access to resources](/azure/role-based-access-control/quickstart-assign-role-user-portal).
+
+| Resource | Role | Assignee | Description |
+|----------|------|----------|-------------|
+| Azure AI Search | Search Services Contributor | Developer's Microsoft Entra ID | List API-Keys to list indexes from Azure OpenAI Studio. |
+| Azure AI Search | Search Index Data Contributor | Developer's Microsoft Entra ID | Required for the indexing scenario. |
+| Azure AI services/OpenAI | Cognitive Services OpenAI Contributor | Developer's Microsoft Entra ID | Call public ingestion API from Azure OpenAI Studio. |
+| Azure AI services/OpenAI | Cognitive Services User | Developer's Microsoft Entra ID | List API-Keys from Azure OpenAI Studio. |
+| Azure AI services/OpenAI | Contributor | Developer's Microsoft Entra ID | Allows for calls to the control plane. |
+| Azure Storage Account | Contributor | Developer's Microsoft Entra ID | List Account SAS to upload files from Azure OpenAI Studio. |
+| Azure Storage Account | Storage Blob Data Contributor | Developer's Microsoft Entra ID | Needed for developers to read and write to blob storage. |
+| Azure Storage Account | Storage File Data Privileged Contributor | Developer's Microsoft Entra ID | Needed to Access File Share in Storage for Promptflow data. |
+| The resource group or Azure subscription where the developer need to deploy the web app to | Contributor | Developer's Microsoft Entra ID | Deploy web app to the developer's Azure subscription. |
 
 ## Use your data in AI Studio