edit for pub

Author: laujan

articles/ai-services/content-understanding/concepts/analyzer-templates.md

@@ -1,10 +1,23 @@
+---
+title: Azure AI Content Understanding analyzer templates
+titleSuffix: Azure AI services
+description: Learn about Azure AI Content Understanding analyzer templates.
+author: laujan
+ms.author: kabrow
+manager: nitinme
+ms.service: azure-ai-content-understanding
+ms.topic: overview
+ms.date: 02/19/2025
+ms.custom: ignite-2024-understanding-release
+---
+
 # Analyzer templates offered with Content Understanding
 
-Content Understanding analyzer templates give you a head start in developing your Content Understanding solution by allowing you to build your analyzer without creating schemas from scratch. 
-There are analyzer templates specific to common scenarios in each modality, such as call center audio analytics and advertising analysis for videos. Templates are also fully customizable, allowing you to adjust any 
-fields in the schemas to better fit your needs. Learn more about the analyzer templates offered for each modality by viewing the tabs below.
+Content Understanding analyzer templates give you a head start in developing your Content Understanding solution by allowing you to build your analyzer without creating schemas from scratch.
+There are analyzer templates specific to common scenarios in each modality, such as call center audio analytics and advertising analysis for videos. Templates are also fully customizable, allowing you to adjust any
+fields in the schemas to better fit your needs. Learn more about the analyzer templates offered for each modality by viewing the following tabs.
 
-The following analyzer templates are available for use in the [Azure AI Foundry Content Understanding experience](https://ai.azure.com/). Check out [Use Content Understanding in the Azure AI Foundry](../quickstart/use-ai-foundry.md) for a step-by-step 
+The following analyzer templates are available for use in the [Azure AI Foundry Content Understanding experience](https://ai.azure.com/). Check out [Use Content Understanding in the Azure AI Foundry](../quickstart/use-ai-foundry.md) for a step-by-step
 on how to create your first analyzer using the below templates.
 
 

articles/ai-services/content-understanding/document/overview.md

@@ -7,7 +7,7 @@ ms.author: lajanuar
 manager: nitinme
 ms.service: azure-ai-content-understanding
 ms.topic: overview
-ms.date: 01/14/2025
+ms.date: 02/19/2025
 ms.custom: ignite-2024-understanding-release
 ---
 

articles/ai-services/content-understanding/how-to/create-multi-service-resource.md

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-content-understanding
 ms.topic: how-to
-ms.date: 11/19/2024
+ms.date: 02/19/2025
 ms.custom: ignite-2024-understanding-release, references_regions
 ms.author: lajanuar
 ---
@@ -25,7 +25,7 @@ To use Content Understanding, you need an Azure AI Services resource. This multi
     :::image type="content" source="../media/overview/azure-multi-service-resource.png" alt-text="Screenshot of the multi-service resource page in the Azure portal.":::
 
     > [!IMPORTANT]
-    > Azure provides more than one resource types named Azure AI services. Be sure to select the one that is listed under Azure AI services → Azure AI services with the logo as shown previously.
+    > Azure provides more than one resource type named Azure AI services. Be sure to select the one that is listed under Azure AI services → Azure AI services with the logo as shown previously.
 
 1. Select the **Create** button.
 
@@ -56,7 +56,7 @@ To use the Azure AI Content Understanding service, you must create your Azure AI
 
 ## Next steps: Try out Content Understanding
 
-Now that you've created your Azure AI Services multi-service resource, you're ready to try out the Content Understanding service.
+Now that you created your Azure AI Services multi-service resource, you're ready to try out the Content Understanding service.
 
 * Check out this Quickstart to [try Content Understanding in the AI Foundry](../quickstart/use-ai-foundry.md).
 * Ready to go straight to code? [Try the Content Understanding REST API](../quickstart/use-rest-api.md).

articles/ai-services/content-understanding/overview.md

@@ -7,7 +7,7 @@ ms.author: lajanuar
 manager: nitinme
 ms.service: azure-ai-content-understanding
 ms.topic: overview
-ms.date: 11/19/2024
+ms.date: 02/19/2025
 ms.custom: ignite-2024-understanding-release
 
 #customer intent: As a user, I want to learn more about Content Understanding solutions.
@@ -51,10 +51,10 @@ Common applications for Content Understanding include:
 
 |Application|Description|Quickstart|
 |:---------|:----------|:----------|
-|Post-call analytics| Businesses and call centers can generate insights from call recordings to track key KPIs, improve product experience, generate business insights, create differentiated customer experiences, and answer queries faster and more accurately.| [**Post-call analytics quickstart**](quickstart/use-ai-foundry.md?tabs=audio#analyzer-templates) |
-|Media asset management| Software and media vendors can use Content Understanding to extract richer, targeted information from videos for media asset management solutions.| [**Media asset management quickstart**](quickstart/use-ai-foundry.md?tabs=video#analyzer-templates) |
-|Tax automation| Tax preparation companies can use Content Understanding to generate a unified view of information from various documents and create comprehensive tax returns.| [**Tax automation quickstart**](quickstart/use-ai-foundry.md?tabs=document#analyzer-templates) | 
-|Chart understanding| Businesses can enhance chart understanding by automating the analysis and interpretation of various types of charts and diagrams using Content Understanding.| [**Chart understanding quickstart**](quickstart/use-ai-foundry.md?tabs=image#analyzer-templates) |
+|Post-call analytics| Businesses and call centers can generate insights from call recordings to track key KPIs, improve product experience, generate business insights, create differentiated customer experiences, and answer queries faster and more accurately.| [**Post-call analytics quickstart**](concepts/analyzer-templates.md#audio) |
+|Media asset management| Software and media vendors can use Content Understanding to extract richer, targeted information from videos for media asset management solutions.| [**Media asset management quickstart**](concepts/analyzer-templates.md#video) |
+|Tax automation| Tax preparation companies can use Content Understanding to generate a unified view of information from various documents and create comprehensive tax returns.| [**Tax automation quickstart**](concepts/analyzer-templates.md#document) | 
+|Chart understanding| Businesses can enhance chart understanding by automating the analysis and interpretation of various types of charts and diagrams using Content Understanding.| [**Chart understanding quickstart**](concepts/analyzer-templates.md#image) |
 
 See [Quickstart](quickstart/use-ai-foundry.md) for more examples.
 

articles/ai-services/content-understanding/quickstart/use-ai-foundry.md

@@ -6,29 +6,29 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-content-understanding
 ms.topic: quickstart
-ms.date: 11/19/2024
+ms.date: 02/19/2025
 ms.custom: ignite-2024-understanding-release
 ---
 
 # Use Content Understanding in the Azure AI Foundry
-[The Azure AI Foundry](https://ai.azure.com/) is a comprehensive platform for developing and deploying generative AI applications and APIs responsibly. Azure AI Content Understanding is a new generative AI-based [Azure AI Service](../../what-are-ai-services.md) that analyzes files of any modality (documents, images, videos, and audio) and extracts structured output in user-defined field formats. This guide shows you how to build and test a Content Understanding analyzer in the AI Foundry, which you can then use in any app or process you build using a simple REST API call. Content Understanding analyzers are fully customizable, and can be created by building your own schema from scratch or by using a suggested analyzer template which are offered to address common scenarios across each data type.
+[The Azure AI Foundry](https://ai.azure.com/) is a comprehensive platform for developing and deploying generative AI applications and APIs responsibly. Azure AI Content Understanding is a new generative [Azure AI Service](../../what-are-ai-services.md) that analyzes files from varied modalities and extracts structured output in a user-defined field format. Input sources include document, video, image, and audio data. This guide shows you how to build and test a Content Understanding analyzer in the AI Foundry. You can then utilize the extracted data in any app or process you build using a simple REST API call. Content Understanding analyzers are fully customizable. You can create an analyzer by building your own schema from scratch or by using a suggested analyzer template offered to address common scenarios across each data type.
 
   :::image type="content" source="../media/quickstarts/ai-foundry-overview.png" alt-text="Screenshot of the Content Understanding workflow in the Azure AI Foundry.":::
 
 ## Prerequisites
 
-To get started, make sure you have the following:
+To get started, make sure you have the following resources and permissions:
 
 * An Azure subscription. If you don't have an Azure subscription, [create a free account](https://azure.microsoft.com/free/) before you begin.
 
-* An Azure AI Foundry hub is required to manage the resources provisioned in your Content Understanding project, and it must be created in one of the following supported regions: westus, swedencentral, or australiaeast. If this is your first time creating a hub, check out [How to create and manage an Azure AI Foundry hub](../../../ai-studio/how-to/create-azure-ai-resource.md?tabs=portal) to learn more. It's important to note you will need the proper permissions to create a hub, or your admin may create one for you.
+* An Azure AI Foundry hub is required to manage the resources provisioned in your Content Understanding project, and it must be created in one of the following supported regions: westus, swedencentral, or australiaeast. If you're creating a hub for the first time, *see* [How to create and manage an Azure AI Foundry hub](../../../ai-studio/how-to/create-azure-ai-resource.md?tabs=portal) to learn more. It's important to note you need the proper permissions to create a hub, or your admin may create one for you.
 
   * If your role is **Contributor** or **Owner**, you can proceed with creating your own hub.
 
   * If your role is **Azure AI Developer**, the hub must already be created before you can complete this quickstart. Your user role must be **Azure AI Developer**, **Contributor**, or **Owner** on the hub. For more information, see [hubs](../../../ai-studio/concepts/ai-resources.md) and [Azure AI roles](../../../ai-studio/concepts/rbac-ai-studio.md).
 
 > [!IMPORTANT]
-> If your organization requires you to customize the security of storage resources, the AI Foundry currently does not currently expose all the features that can be configured. Please refer to [Azure AI services API access keys](../../../ai-studio/concepts/ai-resources.md#azure-ai-services-api-access-keys.md) to create resources that meet your organizations requirements through the Azure portal. Policy enforced in Azure on the hub scope applies to all projects managed under it. To learn how to utilize customer managed keys, refer to [(Preview) Service-side storage of encrypted data when using customer-managed keys](../../../ai-studio/concepts/encryption-keys-portal#preview-service-side-storage-of-encrypted-data-when-using-customer-managed-keys). Please return here when you have resources created.
+> If your organization requires you to customize the security of storage resources, the AI Foundry doesn't currently expose all the features that can be configured. Refer to [Azure AI services API access keys](../../../ai-studio/concepts/encryption-keys-portal.md) to create resources that meet your organizations requirements through the Azure portal. Policy enforced in Azure on the hub scope applies to all projects managed under it. To learn how to utilize customer managed keys, refer to [(Preview) Service-side storage of encrypted data when using customer-managed keys](../../../ai-studio/concepts/encryption-keys-portal.md#preview-service-side-storage-of-encrypted-data-when-using-customer-managed-keys). Return here when you have resources created.
 
 ## Create your first Content Understanding project in the AI Foundry
 
@@ -43,22 +43,19 @@ In order to try out [the Content Understanding service in the AI Foundry](https:
 * The [AI Services landing page](https://ai.azure.com/https://ai.azure.com/explore/aiservices)
    :::image type="content" source="../media/quickstarts/cu-ai-services-landing-page.png" alt-text="Screenshot of the AI Services landing page in AI Foundry.":::
 
-Once on the Content Understanding page, select `Create a new Content Understanding Project`, shown below:
+Once on the Content Understanding page, select `Create a new Content Understanding Project`:
 
    :::image type="content" source="../media/quickstarts/cu-landing-page.png" alt-text="Screenshot of Content Understanding page.":::
 
-Follow the steps in the project creation wizard, and start by selecting the Hub that you already created. When the hub was created, it should have provisioned an AI Services resource, as well as a blob storage container, and these will be selected by default. You can alternatively create one using the wizard, or the [Azure Portal](../how-to/create-multi-service-resource.md). The below diagram illustrates the role of hubs, resources, and projects in the AI Foundry.
+ Follow the steps in the project creation wizard, and start by selecting the hub that you already created. When the hub was created, it should provision an AI Services resource and a blob storage container which are selected by default. You can alternatively create one using the wizard, or the [Azure portal](../how-to/create-multi-service-resource.md). The following diagram illustrates the role of hubs, resources, and projects in the AI Foundry.
 
    :::image type="content" source="../media/quickstarts/foundry-architecture.png" alt-text="Diagram of hub, project, and resource architecture.":::
 
- > [!IMPORTANT]
-> If your organization requires you to customize the security of storage resources, the AI Foundry currently does not currently expose all the features that can be configured. Please refer to [Azure AI services API access keys](../../../ai-studio/concepts/ai-resources.md#azure-ai-services-api-access-key.mds) to create resources that meet your organizations requirements through the Azure portal. Policy enforced in Azure on the hub scope applies to all projects managed under it. Please return here when you have resources created.
-
  Once you complete the setup steps, select `Create project`.
 
  ## Sharing your content understanding project
 
-In order to share and manage access to the Content Understanding project you just created, navigate to the Management Center, found at the bottom of the navigation for your project:
+In order to share and manage access to the Content Understanding project you created, navigate to the Management Center, found at the bottom of the navigation for your project:
 
   :::image type="content" source="../media/quickstarts/cu-find-management-center.png" alt-text="Screenshot of where to find management center.":::
 
@@ -69,13 +66,13 @@ You can manage the users and their individual roles here:
 
 ## Build your first analyzer
 
-Now that everything is configured to get started, the following is a step-by-step walkthrough of how to build your first analyzer, starting with building the schema. The schema is the customziable framework that allows the analyzer to extract insights from your data. In this example, the schema is created to extract key data from an invoice document, but you can bring in any type of data and the steps to follow will remain the same. For a complete list of supported file types, see [input file limits](../service-limits.md#input-file-limits).
+Now that everything is configured to get started, we can walk through, step-by-step, how to build your first analyzer, starting with building the schema. The schema is the customizable framework that allows the analyzer to extract insights from your data. In this example, the schema is created to extract key data from an invoice document, but you can bring in any type of data and the steps remain the same. For a complete list of supported file types, see [input file limits](../service-limits.md#input-file-limits).
 
 1. Upload a sample file of an invoice document or any other data relevant to your scenario.
 
    :::image type="content" source="../media/analyzer-template/define-schema-upload.png" alt-text="Screenshot of upload step in user experience.":::
 
-1. Next, the Content Understanding service will suggest analyzer templates based on your content type. Check out [Analyzer templates offered with Content Understanding](../concepts/analyzer-templates.md) for a full list of all templates offered for each modality. For this example, select **Document analysis** to build your own schema tailored to the invoice scenario. When using your own data, select the analyzer template that best fits your needs, or create your own. See [Analyzer templates](#analyzer-templates) for a full list of available templates.
+1. Next, the Content Understanding service suggests analyzer templates based on your content type. Check out [Analyzer templates offered with Content Understanding](../concepts/analyzer-templates.md) for a full list of all templates offered for each modality. For this example, select **Document analysis** to build your own schema tailored to the invoice scenario. When using your own data, select the analyzer template that best fits your needs, or create your own. See [Analyzer templates](../concepts/analyzer-templates.md) for a full list of available templates.
 
 1. Select **Create**.
 
@@ -95,15 +92,15 @@ Now that everything is configured to get started, the following is a step-by-ste
 
    :::image type="content" source="../media/analyzer-template/define-schema.png" alt-text="Screenshot of completed schema.":::
 
-1. With the completed schema, Content Understanding now generates the output on your sample data. At this step, you can add additional data to test the analyzer's accuracy or make changes to the schema if needed.
+1. With the completed schema, Content Understanding now generates the output on your sample data. At this step, you can add more data to test the analyzer's accuracy or make changes to the schema if needed.
 
    :::image type="content" source="../media/analyzer-template/test-analyzer.png" alt-text="Screenshot of schema testing step.":::
 
 1. Once you're satisfied with the quality of your output, select **Build analyzer**. This action creates an analyzer ID that you can integrate into your own applications, allowing you to call the analyzer from your code.
 
    :::image type="content" source="../media/analyzer-template/build-analyzer.png" alt-text="Screenshot of built analyzer.":::
 
-Now you've successfully built your first Content Understanding analyzer, and are ready to start extracting insights from your data. Check out [Quickstart: Azure AI Content Understanding REST APIs](./use-rest-api.md) to utilize the REST API to call your analyzer.
+Now you successfully built your first Content Understanding analyzer, and are ready to start extracting insights from your data. Check out [Quickstart: Azure AI Content Understanding REST APIs](./use-rest-api.md) to utilize the REST API to call your analyzer.
 
 
 ## Next steps

articles/ai-services/content-understanding/toc.yml

@@ -48,11 +48,11 @@ items:
   - name: Video
     href: video/overview.md
     displayName: video, audio, voice, recognition, synthesis, speaker, identification, verification, diarization, transcription, translation, language, understanding, sentiment, analysis, emotion, detection, pronunciation, model
-  - name: Concepts
-    items:
-      - name: Azure AI Foundry analyzer templates
-        displayName: analyzer, templates, document, text, images, video, audio, multimodal, visual, structured, content, field, extraction
-        href: concepts/analyzer-templates.md
+- name: Concepts
+  items:
+    - name: Azure AI Foundry analyzer templates
+      displayName: analyzer, templates, document, text, images, video, audio, multimodal, visual, structured, content, field, extraction
+      href: concepts/analyzer-templates.md
 - name: Responsible AI
   items:
     - name: Transparency note