@@ -519,13 +519,13 @@ services:
       apikey: ${FORM_RECOGNIZER_KEY}
       billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
       Logging:Console:LogLevel:Default: Information
-      SharedRootFolder: /shared
-      Mounts:Shared: /shared
+      SharedRootFolder: /share
+      Mounts:Shared: /share
       Mounts:Output: /logs
     volumes:
       - type: bind
         source: ${SHARED_MOUNT_PATH}
-        target: /shared
+        target: /share
       - type: bind
         source: ${OUTPUT_MOUNT_PATH}
         target: /logs
@@ -544,13 +544,13 @@ services:
       apikey: ${FORM_RECOGNIZER_KEY}
       billing: ${FORM_RECOGNIZER_ENDPOINT_URI}
       Logging:Console:LogLevel:Default: Information
-      SharedRootFolder: /shared
-      Mounts:Shared: /shared
+      SharedRootFolder: /share
+      Mounts:Shared: /share
       Mounts:Output: /logs
     volumes:
       - type: bind
         source: ${SHARED_MOUNT_PATH}
-        target: /shared
+        target: /share
       - type: bind
         source: ${OUTPUT_MOUNT_PATH}
         target: /logs

@@ -7,7 +7,7 @@ metadata:
   ms.service: azure-ai-document-intelligence
   ms.custom: references_regions
   ms.topic: faq
-  ms.date: 11/19/2024
+  ms.date: 01/14/2025
   ms.author: lajanuar
 title: Frequently asked questions
 summary: |
@@ -62,7 +62,7 @@ sections:
          Can Document Intelligence help with semantic chunking within documents for retrieval-augmented generation?
         answer: |
           **Yes.**
-          
+
           Document Intelligence can provide the building blocks to enable semantic chunking. Semantic chunking is a key step in retrieval-augmented generation (RAG) to ensure context dense chunks and relevance improvement.
 
           - Document Intelligence provides a layout model that provides a visual decomposition of the document into lines, paragraphs, sections, headers, and footers.
@@ -133,14 +133,14 @@ sections:
         answer: |
          **Yes.**
 
-         If your Document Intelligence resource is configured with a firewall or virtual network, you need to add the dedicated IP address 20.3.165.95 to the firewall allowlist for your Document Intelligence resource. Some functions in custom projects (for example, autolabel, project management and human in the loop) don't work if the public network access is disabled.
+         For `v4.0 11-30-2024 (GA)`, auto labeling is hosted natively with the rest of the service, so there's no need for IP allowlisting. For any previous version, if your Document Intelligence resource is configured with a firewall or virtual network, you need to add the dedicated IP address 20.3.165.95 to the firewall allowlist for your Document Intelligence resource. Some functions in custom projects (for example, autolabel, project management and human in the loop) don't work if the public network access is disabled.
 
       - question: |
          When I upload a file in Document Intelligence Studio by "Fetch from URL" function, can I use a URL from my blob storage?
 
         answer: |
-         **Yes.** 
-         
+         **Yes.**
+
          If your Azure blob storage URL includes a SAS token, and is accessible from public networks. You can't use the **Fetch** function for storage accounts where the key access is disabled or behind a firewall/VNet.
 
       - question: |
@@ -171,7 +171,7 @@ sections:
 
           Document Intelligence offers the latest development options within the following platforms:
 
-          - [REST API](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31 &preserve-view=true&tabs=HTTP)
+          - [REST API](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP)
 
           - [Document Intelligence Studio](https://formrecognizer.appliedai.azure.com/studio)
 
@@ -295,14 +295,21 @@ sections:
 
         The copy operation is limited to copying models within the specific cloud environment where you trained the model. For instance, copying models from the public cloud to the Azure Government cloud isn't supported.
 
+    - question: |
+        Am I charged when using auto labeling?
+
+      answer: |
+        **Yes.**
+         Auto label incurs a cost which is equivalent to an analyze request for the corresponding model for a document.
+
     - question: |
         Am I charged when training a custom models?
       answer: |
         **Yes.**
 
-        For `v4.0 11-30-2024 (GA)` custom neural models can be trained for free for a **maximum of 10 hours**. Whether you're training a single model for the 10 hours, or training multiple models for the total of 10 hours, you aren't charged for the first 10 hours. After using up the free 10 hours, you're **automatically charged by the extra training hour**. For details on the prices, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/). This new paid training feature enables training models for an extended duration to process larger documents. For more information on this paid training feature, check [custom neural model billing section](train/custom-neural.md#billing).
-        
-        For `v3.0 2022-08-31` or `v3.1 2023-07-31`, custom neural models can be trained for free for a maximum of 20 training sessions, with each session capped at 30 minutes of training duration. Once you use up all of the 20 training sessions, you can submit Azure support ticket to increase the training session limit. To increase the limit, two training sessions are considered as one training hour, and you're charged per two sessions / one training hour. For details on the prices, refer to the [pricing page]. For more information on ways to increase the limit, check [custom neural model billing section](train/custom-neural.md#billing). **For `v3.0` and `v3.1`, paid training feature is unavailable. Paid training feature for custom neural model is only available on `v4.0`.**
+        For `v4.0 11-30-2024 (GA)` custom neural models can be trained for free for a **maximum of 10 hours**. Whether you're training a single model for the 10 hours, or training multiple models for the total of 10 hours, you aren't charged for the first 10 hours. After using up the free 10 hours, you're **automatically charged by the extra training hour**. For details on prices, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/). This new paid training feature enables training models for an extended duration to process larger documents. For more information on this paid training feature, check [custom neural model billing section](train/custom-neural.md#billing).
+
+        For `v3.0 2022-08-31` or `v3.1 2023-07-31`, custom neural models can be trained for free for a maximum of 20 training sessions, with each session capped at 30 minutes of training duration. Once you use up all of the 20 training sessions, you can submit Azure support ticket to increase the training session limit. To increase the limit, two training sessions are considered as one training hour, and you're charged per two sessions / one training hour. For details on the prices, refer to the [pricing page](https://azure.microsoft.com/pricing/details/ai-document-intelligence/). For more information on ways to increase the limit, check [custom neural model billing section](train/custom-neural.md#billing). **For `v3.0` and `v3.1`, paid training feature is unavailable. Paid training feature for custom neural model is only available on `v4.0`.**
 
   - name: Storage account
     questions:

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 11/19/2024
+ms.date: 01/14/2025
 ms.author: lajanuar
 ms.custom:
   - references_regions
@@ -20,7 +20,6 @@ monikerRange: '>=doc-intel-3.0.0'
 
 # Document Intelligence custom neural model
 
-
 :::moniker range="doc-intel-4.0.0"
 **This content applies to:**![checkmark](../media/yes-icon.png) **v4.0 (GA)** | **Previous versions:** ![blue-checkmark](../media/blue-yes-icon.png) [**v3.1 (GA)**](?view=doc-intel-3.1.0&preserve-view=tru) ![blue-checkmark](../media/blue-yes-icon.png) [**v3.0 (GA)**](?view=doc-intel-3.0.0&preserve-view=tru)
 ::: moniker-end
@@ -45,7 +44,7 @@ Custom neural models share the same labeling format and strategy as [custom temp
 ## Model capabilities
 
  > [!IMPORTANT]
- > Custom neural v4.0 `2024-11-30` (GA) model supports overlapping fields and table cell confidence.
+ > Custom neural v4.0 `2024-11-30` (GA) model supports signature detection, table cell confidence, and overlapping fields.
 
 Custom neural models currently support key-value pairs and selection marks and structured fields (tables).
 
@@ -62,22 +61,9 @@ The `Build` operation supports *template* and *neural* custom models. Previous v
 
 Neural models support documents that have the same information, but different page structures. Examples of these documents include United States W2 forms, which share the same information, but can vary in appearance across companies. For more information, *see* [Custom model build mode](custom-model.md#build-mode).
 
-### Overlapping fields
-
-Custom neural v4.0 `2024-11-30` (GA) model supports overlapping fields:
-
-To use the overlapping fields, your dataset needs to contain at least one sample with the expected overlap. To label an overlap, use **region labeling** to designate each of the spans of content (with the overlap) for each field. Labeling an overlap with field selection (highlighting a value) fails in the Studio as region labeling is the only supported labeling tool for indicating field overlaps. Overlap support includes:
-
-* Complete overlap. The same set of tokens are labeled for two different fields.
-* Partial overlap. Some tokens belong to both fields, but there are tokens that are only part of one field or the other.
-
-Overlapping fields have some limits:
-
-* Any token or word can only be labeled as two fields.
-* overlapping fields in a table can't span table rows.
-* Overlapping fields can only be recognized if at least one sample in the dataset contains overlapping labels for those fields.
+### Signature detection
 
-To use overlapping fields, label your dataset with the overlaps and train the model with the API version ``**2024-11-30 (GA)**``.
+Custom neural v4.0 2024-11-30 (GA) model supports signature detection. To label a signature, use field type as Signature and draw the regions for signature. Signature field only supports one draw region per field. To train a custom neural model with signature detection, you need to use at least **five samples** with signature labeled along with variations to get the most accurate results.
 
 ## Tabular fields
 
@@ -94,7 +80,7 @@ Tabular fields support **cross page tables** by default:
 
 Tabular fields are also useful when extracting repeating information within a document that isn't recognized as a table. For example, a repeating section of work experiences in a resume can be labeled and extracted as a tabular field.
 
-Tabular fields provide **table, row and cell confidence** with the ``**2024-11-30 (GA)**`` API:
+Tabular fields provide **table, row and cell confidence** with the `2024-11-30 (GA)` API:
 
 * Fixed or dynamic tables add confidence support for the following elements:
   * Table confidence, a measure of how accurately the entire table is recognized.
@@ -103,6 +89,23 @@ Tabular fields provide **table, row and cell confidence** with the ``**2024-11-3
 
 * The recommended approach is to review the accuracy in a top-down manner starting with the table first, followed by the row and then the cell. See  [confidence and accuracy scores](../concept/accuracy-confidence.md) to learn more about table, row, and cell confidence.
 
+### Overlapping fields
+
+Custom neural v4.0 2024-11-30 (GA) model supports overlapping fields:
+
+To use the overlapping fields, your dataset needs to contain at least one sample with the expected overlap. To label an overlap, use **region labeling** to designate each of the spans of content (with the overlap) for each field. Labeling an overlap with field selection (highlighting a value) fails in the Studio as region labeling is the only supported labeling tool for indicating field overlaps. Overlap support includes:
+
+* Complete overlap. The same set of tokens are labeled for two different fields.
+* Partial overlap. Some tokens belong to both fields, but there are tokens that are only part of one field or the other.
+
+Overlapping fields have some limits:
+
+* Any token or word can only be labeled as two fields.
+* overlapping fields in a table can't span table rows.
+* Overlapping fields can only be recognized if at least one sample in the dataset contains overlapping labels for those fields.
+
+To use overlapping fields, label your dataset with the overlaps and train the model with the API version ``**2024-11-30 (GA)**``.
+
 ### Supported languages and locales
 
 *See* our [Language Support—custom models](../language-support/custom.md#custom-neural) for a complete list of supported languages.
@@ -203,7 +206,7 @@ Custom neural models differ from custom template models in a few different ways.
 
 * Custom neural model doesn't recognize values split across page boundaries.
 * Custom neural unsupported field types are ignored if a dataset labeled for custom template models is used to train a custom neural model.
-* Custom neural models are limited to 20 build operations per month. Open a support request if you need the limit increased. For more information, see [Document Intelligence service quotas and limits](../service-limits.md).
+* Custom neural models are limited to 20 build operations per month for versions 3.x. Open a support request if you need the limit increased. For more information, see [Document Intelligence service quotas and limits](../service-limits.md).
 
 ## Training a model
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: whats-new
-ms.date: 12/17/2024
+ms.date: 01/14/2025
 ms.author: lajanuar
 ms.custom:
   - references_regions
@@ -25,11 +25,11 @@ ms.custom:
 Document Intelligence service is updated on an ongoing basis. Bookmark this page to stay up to date with release notes, feature enhancements, and our newest documentation.
 
 > [!IMPORTANT]
-> Preview API versions are retired once the GA API is released. The 2023-02-28-preview API version is being retired, if you are still using the preview API or the associated SDK versions, please update your code to target the latest API version 2024-11-30 (GA). </br>
+> Preview API versions are retired once the GA API is released. The 2023-02-28-preview API version is retiring. If you're still using the preview API or the associated SDK versions, update your code to target the latest API version `2024-11-30 (GA)`. </br>
 
 ## December 2024
 
-**Document Intelligence v4.0 programming language SDKs are now generally available (GA)**! <br><br>The latest client SDKs default to the [**2024-11-30 REST API (GA)**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30)&preserve-view=true) version of the service.<br><br>
+**Document Intelligence v4.0 programming language SDKs are now generally available (GA)**! <br><br>The latest client libraries default to the [**2024-11-30 REST API (GA)**](/rest/api/aiservices/operation-groups?view=rest-aiservices-v4.0%20(2024-11-30&preserve-view=true) version of the service.<br><br>
 For more information, *see* client libraries for the following supported programming languages:
 
 * [🆕 .NET (C#)](versioning/changelog-release-history.md?view=doc-intel-4.0.0&tabs=csharp&preserve-view=true)
@@ -53,12 +53,13 @@ For more information, *see* client libraries for the following supported program
 * 🆕 Searchable PDF. The [prebuilt read](prebuilt/read.md) model now supports images formats (JPEG/JPG, PNG, BMP, TIFF, HEIF)  and language expansion to include Chinese, Japanese, and Korean for  [PDF output](prebuilt/read.md#searchable-pdf).
  
 * [Custom classification model](train/custom-model.md#custom-classification-model)
-  * Custom classification model supports incremental training. You can add new samples to exisisting classes or add new classes by referencing an existing classifier. 
+  * Custom classification model supports incremental training. You can add new samples to existing classes or add new classes by referencing an existing classifier. 
   * With v4.0, custom classification model doesn't split documents by default during analysis. You need to explicitly set 'splitMode' property to auto to preserve the older behavior.
   * Custom classification model now supports 25,000 pages as new training page limit.
 
 * [Custom Neural Model](train/custom-neural.md)
   * Custom Neural model now supports signature detection.
+  * Custom neural models support paid training for longer duration when you need to train model with a larger labeled dataset. The first 20 training runs in a calendar month continue to be free. Any training operations over 20 is on the paid tier. Learn more details on [billing](train/custom-neural.md#billing).
 
 * [ US Bank statement model](concept-bank-statement.md)
   * US Bank Statement Model now supports check table extraction.
@@ -221,14 +222,13 @@ The Document Intelligence [**2023-10-31-preview**](/rest/api/aiservices/document
   * Add-on capabilities are available within all models excluding the [Read model](prebuilt/read.md).
 
 >[!NOTE]
-> With the 2022-08-31 API general availability (GA) release, the associated preview APIs are being deprecated. If you are using the 2021-09-30-preview, the 2022-01-30-preview or he 2022-06-30-preview API versions, please update your applications to target the 2022-08-31 API version. There are a few minor changes involved, for more information, _see_ the [migration guide](v3-1-migration-guide.md).
+> With the 2022-08-31 API general availability (GA) release, the associated preview APIs are being deprecated. If you're using the 2021-09-30-preview, 2022-01-30-preview, or 2022-06-30-preview API versions, update your applications to target the 2022-08-31 API version. There are a few minor changes involved, for more information, _see_ the [migration guide](v3-1-migration-guide.md).
 
 ## July 2023
 
 > [!NOTE]
 > Form Recognizer is now **Azure AI Document Intelligence**!
 >
-> * Document, Azure AI services encompass all of what were previously known as Cognitive Services and Azure Applied AI Services.
 > * There are no changes to pricing.
 > * The names *Cognitive Services* and *Azure Applied AI* continue to be used in Azure billing, cost analysis, price list, and price APIs.
 > * There are no breaking changes to application programming interfaces (APIs) or client libraries.
@@ -265,7 +265,7 @@ The v3.1 API introduces new and updated capabilities:
     :::image type="content" source="media/studio/analyze-options.gif" alt-text="Animated screenshot showing use of the analyze-options button to configure options in Studio.":::
 
     > [!NOTE]
-    > Font extraction is not visualized in Document Intelligence Studio. However, you can check the styles section of the JSON output for the font detection results.
+    > Font extraction isn't visualized in Document Intelligence Studio. However, you can check the styles section of the JSON output for the font detection results.
 
 ✔️ **Auto labeling documents with prebuilt models or one of your own models**
 
@@ -496,7 +496,7 @@ The v3.1 API introduces new and updated capabilities:
 ## September 2022
 
 >[!NOTE]
-> Starting with version 4.0.0, a new set of clients has been introduced to leverage the newest features of the Document Intelligence service.
+> Starting with version 4.0.0, a new set of clients is introduced to apply the newest features of the Document Intelligence service.
 
 **SDK version 4.0.0 GA release includes the following updates:**
 

@@ -50,7 +50,8 @@ The following data is stored on the managed resources.
 A new architecture for customer-managed key encryption with hubs is available in preview, which resolves the dependency on the managed resource group. In this new model, encrypted data is stored service-side on Microsoft-managed resources instead of in managed resources in your subscription. Metadata is stored in multitenant resources using document-level CMK encryption. An Azure AI Search instance is hosted on the Microsoft-side per customer, and for each hub. Due to its dedicated resource model, its Azure cost is charged in your subscription via the hub resource.
 
 > [!NOTE]
-> During this preview key rotation and user-assigned identity capabilities are not supported. Service-side encryption is currently not supported in reference to an Azure Key Vault for storing your encryption key that has public network access disabled.
+> - During this preview key rotation and user-assigned identity capabilities are not supported. Service-side encryption is currently not supported in reference to an Azure Key Vault for storing your encryption key that has public network access disabled.
+> - If you are using the preview server-side storage, Azure charges will continue to accrue during the soft delete retention period.
 
 ## Use customer-managed keys with Azure Key Vault
 
@@ -97,6 +98,7 @@ Alternatively, use infrastructure-as-code options for automation. Example Bicep
 * At the time of creation, you can't provide or modify resources that are created in the Microsoft-managed Azure resource group in your subscription.
 * You can't delete Microsoft-managed resources used for customer-managed keys without also deleting your hub.
 * [Azure AI services Customer-Managed Key Request Form](https://aka.ms/cogsvc-cmk) is still required for Speech and Content Moderator.
+* If you are using the [server-side preview](#preview-service-side-storage-of-encrypted-data-when-using-customer-managed-keys), Azure charges will continue to accrue during the soft delete retention period.
 
 ## Related content
 

@@ -63,9 +63,9 @@ Models labeled _Retired_ are no longer available for use. You can't create new d
 
 - Members of the _owner_, _contributor_, _reader_, monitoring contributor_, and _monitoring reader_ roles for each Azure subscription with a serverless API model deployment receive a notification when a model deprecation is announced. The notification contains the dates when the model enters legacy, deprecated, and retired states. The notification might provide information about possible replacement model options, if applicable.
 
+The following table lists the timelines for models that are on track for retirement. The specified dates are in UTC time.
 
-
-| Model provider | Model | Legacy date | Deprecation date | Retirement date | Suggested replacement model |
+| Model provider | Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |
 | ---- | ---- | ---- | --- | ---- | --- |
 | Mistral AI | [Mistral-large-2407](https://aka.ms/azureai/landing/Mistral-Large-2407) | January 13, 2025 | February 13, 2025 | May 13, 2025 | [Mistral-large-2411](https://aka.ms/aistudio/landing/Mistral-Large-2411) |
 | Mistral AI | [Mistral-large](https://aka.ms/azureai/landing/Mistral-Large) | December 15, 2024 | January 15, 2025 | April 15, 2025 | [Mistral-large-2407](https://aka.ms/azureai/landing/Mistral-Large-2407) |

@@ -6,7 +6,7 @@ manager: scottpolly
 ms.service: azure-ai-studio
 ms.custom: ignite-2023, devx-track-azurecli, build-2024, ignite-2024
 ms.topic: how-to
-ms.date: 12/05/2024
+ms.date: 01/15/2025
 ms.reviewer: meerakurup
 ms.author: larryfr
 author: Blackmist
@@ -37,17 +37,29 @@ You get several hub default resources in your resource group. You need to config
 
 ## Create a hub that uses a private endpoint
 
-Use one of the following methods to create a hub with a private endpoint. Each of these methods __requires an existing virtual network__:
+If you are creating a new hub, use the following tabs to select how you are creating the hub (Azure portal or Azure CLI.) Each of these methods __requires an existing virtual network__:
 
 # [Azure portal](#tab/azure-portal)
 
-1. From the [Azure portal](https://portal.azure.com), go to Azure AI Foundry and choose __+ New Azure AI__.
-1. Choose network isolation mode in __Networking__ tab.
+> [!NOTE]
+> The information in this document is only about configuring a private link. For a walkthrough of creating a secure hub in the portal, see [Create a secure hub in the Azure portal](create-secure-ai-hub.md).
+
+1. From the [Azure portal](https://portal.azure.com), search for __Azure AI Foundry__ and create a new resource by selecting __+ New Azure AI__.
+1. After configuring the __Basics__ and __Storage__ tabs, select the __Networking__ tab and pick the __Network isolation__ option that best suits your needs.
+
+    :::image type="content" source="../media/how-to/network/ai-hub-networking.png" alt-text="Screenshot of the Create a hub with the option to set network isolation information." lightbox="../media/how-to/network/ai-hub-networking.png":::
+
 1. Scroll down to __Workspace Inbound access__ and choose __+ Add__.
+
+    :::image type="content" source="../media/how-to/network/workspace-inbound-access.png" alt-text="Screenshot of the workspace inbound access section." lightbox="../media/how-to/network/workspace-inbound-access.png":::
+
 1. Input required fields. When selecting the __Region__, select the same region as your virtual network.
 
 # [Azure CLI](#tab/cli)
 
+> [!NOTE]
+> The information in this section doesn't cover basic hub configuration. For more information, see [Create a hub using the Azure CLI](./develop/create-hub-project-sdk.md?tabs=azurecli).
+
 After creating the hub, use the [Azure networking CLI commands](/cli/azure/network/private-endpoint#az-network-private-endpoint-create) to create a private link endpoint for the hub.
 
 ```azurecli-interactive
@@ -113,12 +125,17 @@ Use one of the following methods to add a private endpoint to an existing hub:
 # [Azure portal](#tab/azure-portal)
 
 1. From the [Azure portal](https://portal.azure.com), select your hub.
-1. From the left side of the page, select __Networking__ and then select the __Private endpoint connections__ tab.
-1. When selecting the __Region__, select the same region as your virtual network.
-1. When selecting __Resource type__, use `azuremlworkspace`.
-1. Set the __Resource__ to your workspace name.
+1. From the left side of the page, select __Settings__, __Networking__, and then select the __Private endpoint connections__ tab. Select __+ Private endpoint__.
+
+    :::image type="content" source="../media/how-to/network/add-private-endpoint.png" alt-text="Screenshot of the private endpoint connections tab":::
 
-Finally, select __Create__ to create the private endpoint.
+1. When going through the forms to create a private endpoint, be sure to:
+
+    - From __Basics__, select the same the __Region__ as your virtual network.
+    - From __Resource__, select `amlworkspace` as the __target sub-resource__.
+    - From the __Virtual Network__ form, select the virtual network and subnet that you want to connect to.
+ 
+1. After populating the forms with any additional network configurations you require, use the __Review + create__ tab to review your settings and select __Create__ to create the private endpoint.
 
 # [Azure CLI](#tab/cli)
 
@@ -192,9 +209,11 @@ To remove a private endpoint, use the following information:
 # [Azure portal](#tab/azure-portal)
 
 1. From the [Azure portal](https://portal.azure.com), select your hub.
-1. From the left side of the page, select __Networking__ and then select the __Private endpoint connections__ tab.
+1. From the left side of the page, select __Settings__, __Networking__, and then select the __Private endpoint connections__ tab.
 1. Select the endpoint to remove and then select __Remove__.
 
+    :::image type="content" source="../media/how-to/network/remove-private-endpoint.png" alt-text="Screenshot of a selected private endpoint with the remove option highlighted.":::
+
 # [Azure CLI](#tab/cli)
 
 When using the Azure CLI, use the following command to remove the private endpoint:

@@ -35,7 +35,7 @@ POST /chat/completions?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 ## Request Body

@@ -33,7 +33,7 @@ POST /completions?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 

@@ -35,7 +35,7 @@ POST /embeddings?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 ## Request Body

@@ -35,7 +35,7 @@ POST /images/embeddings?api-version=2024-04-01-preview
 
 | Name | Required | Type | Description |
 | --- | --- | --- | --- |
-| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `ignore` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
+| extra-parameters | | string | The behavior of the API when extra parameters are indicated in the payload. Using `pass-through` makes the API to pass the parameter to the underlying model. Use this value when you want to pass parameters that you know the underlying model can support. Using `drop` makes the API to drop any unsupported parameter. Use this value when you need to use the same payload across different models, but one of the extra parameters may make a model to error out if not supported. Using `error` makes the API to reject any extra parameter in the payload. Only parameters specified in this API can be indicated, or a 400 error is returned. |
 | azureml-model-deployment |     | string | Name of the deployment you want to route the request to. Supported for endpoints that support multiple deployments. |
 
 ## Request Body

@@ -5,7 +5,7 @@ description: This article lists Azure AI Foundry feature availability across clo
 manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: conceptual
-ms.date: 5/21/2024
+ms.date: 01/15/2025
 ms.reviewer: deeikele
 ms.author: sgilley
 author: sdgilley
@@ -14,28 +14,28 @@ ms.custom: references_regions, build-2024
 
 # Azure AI Foundry feature availability across clouds regions
 
-[!INCLUDE [feature-preview](../includes/feature-preview.md)]
-
 Azure AI Foundry brings together various Azure AI capabilities that previously were only available as standalone Azure services. While we strive to make all features available in all regions where Azure AI Foundry is supported at the same time, feature availability may vary by region. In this article, you'll learn what Azure AI Foundry features are available across cloud regions.  
 
 ## Azure Public regions
 
-Azure AI Foundry is currently available in the following Azure regions. You can create [Azure AI Foundry hubs](../how-to/create-azure-ai-resource.md) and Azure AI Foundry projects in these regions.
+Azure AI Foundry is currently available in the following Azure regions. You can create [projects in Azure AI Foundry portal](../how-to/create-projects.md) in these regions.
 
 - Australia East
 - Brazil South
 - Canada Central
+- Canada East
 - East US
 - East US 2
 - France Central
 - Germany West Central
-- India South
 - Japan East
+- Korea Central
 - North Central US
 - Norway East
 - Poland Central
 - South Africa North
 - South Central US
+- South India
 - Sweden Central
 - Switzerland North
 - UAE North
@@ -65,6 +65,10 @@ Azure AI Speech capabilities including custom neural voice vary in regional avai
 
 Some models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. For information on the regions where each model is available, see [Region availability for models in Serverless API endpoints](../how-to/deploy-models-serverless-availability.md).
 
+## Azure AI Content Safety
+
+To use the Content Safety APIs, you must create your Azure AI Content Safety resource in a supported region. For a list of supported regions, see [What is Azure AI Content Safety?](../../ai-services/content-safety/overview.md#region-availability)
+
 ## Next steps
 
 - See [Azure global infrastructure products by region](https://azure.microsoft.com/global-infrastructure/services/).