@@ -6,36 +6,39 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: how-to
-ms.date: 01/22/2025
+ms.date: 04/03/2025
 ms.author: lajanuar
 ---
 
-
 # Install and run containers
 
 <!-- markdownlint-disable MD024 -->
 <!-- markdownlint-disable MD051 -->
 
 :::moniker range=">=doc-intel-2.1.0"
 
-**This content applies to:** ![checkmark](../media/yes-icon.png) **v3.0 (GA)** ![checkmark](../media/yes-icon.png) **v3.1 (GA)**
+**This content applies to:** ![checkmark](../media/yes-icon.png) **v3.0 (GA)** ![checkmark](../media/yes-icon.png) **v3.1 (GA)** ![checkmark](../media/yes-icon.png) **v4.0 (GA)**
 
 Azure AI Document Intelligence is an Azure AI service that lets you build automated data processing software using machine-learning technology. Document Intelligence enables you to identify and extract text, key/value pairs, selection marks, table data, and more from your documents. The results are delivered as structured data that ../includes the relationships in the original file. Containers process only the data provided to them and solely utilize the resources they're permitted to access. Containers can't process data from other regions.
 
 In this article you can learn how to download, install, and run Document Intelligence containers. Containers enable you to run the Document Intelligence service in your own environment. Containers are great for specific security and data governance requirements.
 
+* **Layout** model is supported by Document Intelligence v3.1 containers.
+
 * **Read**, **Layout**,  **ID Document**,  **Receipt**, and **Invoice**  models are supported by Document Intelligence v3.1 containers.
 
 * **Read**, **Layout**, **General Document**, **Business Card**, and **Custom** models are supported by Document Intelligence v3.0 containers.
 
 ## Version support
 
-Support for containers is currently available with Document Intelligence version `v3.0: 2022-08-31 (GA)` for all models and `v3.1 2023-07-31 (GA)` for Read, Layout, ID Document, Receipt, and Invoice models:
+Support for containers is currently available with Document Intelligence version `v3.0: 2022-08-31 (GA)` for all models, `v3.1 2023-07-31 (GA)` for Read, Layout, ID Document, Receipt, and Invoice models, and `v4.0 2024-11-30 (GA)` for Layout:
 
 * [REST API `v3.0: 2022-08-31 (GA)`](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v3.0%20(2022-08-31)&preserve-view=true&tabs=HTTP)
 * [REST API `v3.1: 2023-07-31 (GA)`](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v3.1%20(2023-07-31)&tabs=HTTP&preserve-view=true)
+* [REST API `v4.0: 2024-11-30 (GA)`](/rest/api/aiservices/document-models/analyze-document?view=rest-aiservices-v4.0%20(2024-11-30)&tabs=HTTP&preserve-view=true)
 * [Client libraries targeting `REST API v3.0: 2022-08-31 (GA)`](../sdk-overview-v3-0.md)
 * [Client libraries targeting `REST API v3.1: 2023-07-31 (GA)`](../sdk-overview-v3-1.md)
+* [Client libraries targeting `REST API v4.0: 2024-11-30 (GA)`](../sdk-overview-v4-0.md)
 
 ## Prerequisites
 
@@ -63,7 +66,7 @@ The host is a x64-based computer that runs the Docker container. It can be a com
 
 > [!NOTE]
 >
-> Note that Studio container cannot be deployed and run in Azure Kubernetes Service. Studio container is only supported to be run on local machine.
+> The Studio container can't be deployed and run in Azure Kubernetes Service. The Studio container is only supported to run on local machines.
 
 ### Container requirements and recommendations
 
@@ -113,7 +116,9 @@ Feature container | Supporting containers |
 >  IMAGE ID         REPOSITORY                TAG
 >  <image-id>       <repository-path/name>    <tag-name>
 >  ```
+::: moniker-end
 
+:::moniker range="doc-intel-4.0.0"
 ## Run the container with the **docker-compose up** command
 
 * Replace the {ENDPOINT_URI} and {API_KEY} values with your resource Endpoint URI and the key from the Azure resource page.
@@ -125,7 +130,37 @@ Feature container | Supporting containers |
 * The `EULA`, `Billing`, and `ApiKey`  values must be specified; otherwise the container can't start.
 
 > [!IMPORTANT]
-> The keys are used to access your Document Intelligence resource. Do not share your keys. Store them securely, for example, using Azure Key Vault. We also recommend regenerating these keys regularly. Only one key is necessary to make an API call. When regenerating the first key, you can use the second key for continued access to the service.
+> The keys are used to access your Document Intelligence resource. Don't share your keys. Store them securely, for example, using Azure Key Vault. We also recommend regenerating these keys regularly. Only one key is necessary to make an API call. When regenerating the first key, you can use the second key for continued access to the service.
+
+### [Layout](#tab/layout)
+
+The following code sample is a self-contained `docker compose`  example to run the Document Intelligence Layout container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Layout container instance.
+
+```yml
+version: "3.9"
+services:
+  azure-form-recognizer-layout:
+    container_name: azure-form-recognizer-layout
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-4.0
+    environment:
+      - EULA=accept
+      - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+      - apiKey={FORM_RECOGNIZER_KEY}
+    ports:
+      - "5000:5000"
+    networks:
+      - ocrvnet
+networks:
+  ocrvnet:
+    driver: bridge
+```
+
+Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
+
+```bash
+docker-compose up
+
+```
 
 ### [Read](#tab/read)
 
@@ -190,6 +225,302 @@ docker-compose up
 
 Given the resources on the machine, the General Document container might take some time to start up.
 
+### [Invoice](#tab/invoice)
+
+The following code sample is a self-contained `docker compose`  example to run the Document Intelligence Invoice container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Invoice and Layout container instances.
+
+You must use 3.1 GA Layout image as an upstream for both 3.0 GA and 3.1 GA Invoice models.
+
+```yml
+version: "3.9"
+services:
+  azure-cognitive-service-invoice:
+    container_name: azure-cognitive-service-invoice
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/invoice-3.1
+    environment:
+        - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+        - AzureCognitiveServiceLayoutHost=http://azure-cognitive-service-layout:5000
+    ports:
+      - "5000:5050"
+  azure-cognitive-service-layout:
+    container_name: azure-cognitive-service-layout
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.1
+    environment:
+        - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+```
+
+Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
+
+```bash
+docker-compose up
+```
+
+### [Receipt](#tab/receipt)
+
+The following code sample is a self-contained `docker compose`  example to run the Document Intelligence General Document container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Receipt and Read container instances.
+
+You can use 3.1 GA Layout image as an upstream instead of Read image.
+
+```yml
+version: "3.9"
+services:
+  azure-cognitive-service-receipt:
+    container_name: azure-cognitive-service-receipt
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/receipt-3.1
+    environment:
+        - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+        - AzureCognitiveServiceReadHost=http://azure-cognitive-service-read:5000
+    ports:
+          - "5000:5050"
+  azure-cognitive-service-read:
+    container_name: azure-cognitive-service-read
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
+    environment:
+      - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+```
+
+Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
+
+```bash
+docker-compose up
+```
+
+### [ID Document](#tab/id-document)
+
+The following code sample is a self-contained `docker compose`  example to run the Document Intelligence General Document container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your ID and Read container instances.
+
+You can use 3.1 GA Layout image as an upstream instead of Read image.
+
+```yml
+version: "3.9"
+services:
+  azure-cognitive-service-id-document:
+      container_name: azure-cognitive-service-id-document
+      image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/id-document-3.1
+      environment:
+          - EULA=accept
+          - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+          - apiKey={FORM_RECOGNIZER_KEY}
+          - AzureCognitiveServiceReadHost=http://azure-cognitive-service-read:5000
+      ports:
+          - "5000:5050"
+  azure-cognitive-service-read:
+      container_name: azure-cognitive-service-read
+      image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
+      environment:
+          - EULA=accept
+          - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+          - apiKey={FORM_RECOGNIZER_KEY}
+```
+
+Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
+
+```bash
+docker-compose up
+```
+
+### [Business Card](#tab/business-card)
+
+```yml
+version: "3.9"
+services:
+  azure-cognitive-service-invoice:
+    container_name: azure-cognitive-service-businesscard
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/businesscard-3.0
+    environment:
+        - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+        - AzureCognitiveServiceLayoutHost=http://azure-cognitive-service-layout:5000
+    ports:
+      - "5000:5050"
+  azure-cognitive-service-layout:
+    container_name: azure-cognitive-service-layout
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0
+    environment:
+        - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+```
+
+
+### [Custom](#tab/custom)
+
+In addition to the [prerequisites](#prerequisites), you need to do the following to process a custom document:
+
+#### Create a folder and store the following files
+
+* [**.env**](#create-an-environment-file)
+* [**nginx.conf**](#create-an-nginx-file)
+* [**docker-compose.yml**](#create-a-docker-compose-file)
+
+#### Create a folder and store your input data
+
+* Name this folder **files**.
+* We reference the file path for this folder as  **{FILE_MOUNT_PATH}**.
+* Copy the file path in a convenient location, you need to add it to your **.env** file. As an example if the folder is called files, located in the same folder as the `docker-compose` file, the .env file entry is `FILE_MOUNT_PATH="./files"`
+
+#### Create a folder to store the logs  written by the Document Intelligence service on your local machine
+
+* Name this folder **output**.
+* We reference the file path for this folder as **{OUTPUT_MOUNT_PATH}**.
+* Copy the file path in a convenient location, you need to add it to your **.env** file. As an example if the folder is called output, located in the same folder as the `docker-compose` file, the .env file entry is `OUTPUT_MOUNT_PATH="./output"`
+
+#### Create a folder for storing internal processing shared between the containers
+
+* Name this folder **shared**.
+* We reference the file path for this folder as  **{SHARED_MOUNT_PATH}**.
+* Copy the file path in a convenient location, you need to add it to your **.env** file. As an example if the folder is called shared, located in the same folder as the `docker-compose` file, the .env file entry is `SHARED_MOUNT_PATH="./share"`
+
+#### Create a folder for the Studio to store project related information
+
+* Name this folder **db**.
+* We reference the file path for this folder as  **{DB_MOUNT_PATH}**.
+* Copy the file path in a convenient location, you need to add it to your **.env** file. As an example if the folder is called db, located in the same folder as the `docker-compose` file, the .env file entry is `DB_MOUNT_PATH="./db"`
+
+#### Create an environment file
+
+  1. Name this file **.env**.
+
+  1. Declare the following environment variables:
+
+```bash
+
+
+SHARED_MOUNT_PATH="./share"
+OUTPUT_MOUNT_PATH="./output"
+FILE_MOUNT_PATH="./files"
+DB_MOUNT_PATH="./db"
+FORM_RECOGNIZER_ENDPOINT_URI="YourFormRecognizerEndpoint"
+FORM_RECOGNIZER_KEY="YourFormRecognizerKey"
+NGINX_CONF_FILE="./nginx.conf"
+```
+
+#### Create an **nginx** file
+
+  1. Name this file **nginx.conf**.
+
+  1. Enter the following configuration:
+
+```bash
+worker_processes 1;
+
+events { worker_connections 1024; }
+
+http {
+
+    sendfile on;
+    client_max_body_size 90M;
+    upstream docker-custom {
+        server azure-cognitive-service-custom-template:5000;
+    }
+
+    upstream docker-layout {
+        server  azure-cognitive-service-layout:5000;
+    }
+
+    server {
+        listen 5000;
+
+        location = / {
+            proxy_set_header Host $host:$server_port;
+            proxy_set_header Referer $scheme://$host:$server_port;
+            proxy_pass http://docker-custom/;
+        }
+
+        location /status {
+            proxy_pass http://docker-custom/status;
+        }
+
+        location /test {
+            return 200 $scheme://$host:$server_port;
+        }
+
+        location /ready {
+            proxy_pass http://docker-custom/ready;
+        }
+
+        location /swagger {
+            proxy_pass http://docker-custom/swagger;
+        }
+
+        location /api-docs {
+            proxy_pass http://docker-custom/api-docs;
+        }
+
+        location /formrecognizer/documentModels/prebuilt-layout {
+            proxy_set_header Host $host:$server_port;
+            proxy_set_header Referer $scheme://$host:$server_port;
+
+            add_header 'Access-Control-Allow-Origin' '*' always;
+            add_header 'Access-Control-Allow-Headers' 'cache-control,content-type,ocp-apim-subscription-key,x-ms-useragent' always;
+            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS' always;
+            add_header 'Access-Control-Expose-Headers' '*' always;
+
+            if ($request_method = 'OPTIONS') {
+                return 200;
+            }
+
+            proxy_pass http://docker-layout/formrecognizer/documentModels/prebuilt-layout;
+        }
+
+        location /formrecognizer/documentModels {
+            proxy_set_header Host $host:$server_port;
+            proxy_set_header Referer $scheme://$host:$server_port;
+
+            add_header 'Access-Control-Allow-Origin' '*' always;
+            add_header 'Access-Control-Allow-Headers' 'cache-control,content-type,ocp-apim-subscription-key,x-ms-useragent' always;
+            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS, DELETE' always;
+            add_header 'Access-Control-Expose-Headers' '*' always;
+
+            if ($request_method = 'OPTIONS') {
+                return 200;
+            }
+
+            proxy_pass http://docker-custom/formrecognizer/documentModels;
+        }
+
+        location /formrecognizer/operations {
+            add_header 'Access-Control-Allow-Origin' '*' always;
+            add_header 'Access-Control-Allow-Headers' 'cache-control,content-type,ocp-apim-subscription-key,x-ms-useragent' always;
+            add_header 'Access-Control-Allow-Methods' 'GET, POST, OPTIONS, PUT, DELETE, PATCH' always;
+            add_header 'Access-Control-Expose-Headers' '*' always;
+
+            if ($request_method = OPTIONS ) {
+                return 200;
+            }
+
+            proxy_pass http://docker-custom/formrecognizer/operations;
+        }
+    }
+}
+
+```
+::: moniker-end
+
+:::moniker range="doc-intel-3.0.0 || doc-intel-3.1.0"
+
+## Run the container with the **docker-compose up** command
+
+* Replace the {ENDPOINT_URI} and {API_KEY} values with your resource Endpoint URI and the key from the Azure resource page.
+
+   :::image type="content" source="../media/containers/keys-and-endpoint.png" alt-text="Screenshot of Azure portal keys and endpoint page.":::
+
+* Ensure that the `EULA` value is set to *accept*.
+
+* The `EULA`, `Billing`, and `ApiKey`  values must be specified; otherwise the container can't start.
+
+> [!IMPORTANT]
+> The keys are used to access your Document Intelligence resource. Don't share your keys. Store them securely, for example, using Azure Key Vault. We also recommend regenerating these keys regularly. Only one key is necessary to make an API call. When regenerating the first key, you can use the second key for continued access to the service.
+
 ### [Layout](#tab/layout)
 
 The following code sample is a self-contained `docker compose`  example to run the Document Intelligence Layout container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Layout container instance.
@@ -219,6 +550,69 @@ Now, you can start the service with the [**docker compose**](https://docs.docker
 docker-compose up
 ```
 
+### [Read](#tab/read)
+
+The following code sample is a self-contained `docker compose`  example to run the Document Intelligence Layout container. With `docker compose`, you use a YAML file to configure your application's services. Then, with the `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Layout container instance.
+
+```yml
+version: "3.9"
+services:
+  azure-form-recognizer-read:
+    container_name: azure-form-recognizer-read
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/read-3.1
+    environment:
+      - EULA=accept
+      - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+      - apiKey={FORM_RECOGNIZER_KEY}
+    ports:
+      - "5000:5000"
+    networks:
+      - ocrvnet
+networks:
+  ocrvnet:
+    driver: bridge
+```
+
+Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
+
+```bash
+docker-compose up
+```
+
+### [General Document](#tab/general-document)
+
+The following code sample is a self-contained `docker compose`  example to run the Document Intelligence General Document container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your General Document and Layout container instances.
+
+```yml
+version: "3.9"
+services:
+  azure-cognitive-service-document:
+    container_name: azure-cognitive-service-document
+    image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/document-3.0
+    environment:
+        - EULA=accept
+        - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+        - apiKey={FORM_RECOGNIZER_KEY}
+        - AzureCognitiveServiceLayoutHost=http://azure-cognitive-service-layout:5000
+    ports:
+      - "5000:5050"
+  azure-cognitive-service-layout:
+     container_name: azure-cognitive-service-layout
+     image: mcr.microsoft.com/azure-cognitive-services/form-recognizer/layout-3.0
+     environment:
+         - EULA=accept
+         - billing={FORM_RECOGNIZER_ENDPOINT_URI}
+         - apiKey={FORM_RECOGNIZER_KEY}
+```
+
+Now, you can start the service with the [**docker compose**](https://docs.docker.com/compose/) command:
+
+```bash
+docker-compose up
+```
+
+Given the resources on the machine, the General Document container might take some time to start up.
+
 ### [Invoice](#tab/invoice)
 
 The following code sample is a self-contained `docker compose`  example to run the Document Intelligence Invoice container. With `docker compose`, you use a YAML file to configure your application's services. Then, with `docker-compose up` command, you create and start all the services from your configuration. Enter {FORM_RECOGNIZER_ENDPOINT_URI} and {FORM_RECOGNIZER_KEY} values for your Invoice and Layout container instances.
@@ -495,8 +889,8 @@ http {
             proxy_pass http://docker-custom/formrecognizer/operations;
         }
     }
-}
 
+}
 ```
 ::: moniker-end
 
@@ -699,7 +1093,7 @@ Custom template containers require a few different configurations and support ot
 |Storage:ObjectStore:AzureBlob:ConnectionString    | No| Azure Blob connection string |
 | HealthCheck:MemoryUpperboundInMB    | No |    Memory threshold for reporting unhealthy to liveness. Default: Same as recommended memory |
 | StorageTimeToLiveInMinutes    | No| `TTL` duration to remove all intermediate and final files. Default: Two days, `TTL` can set between five minutes to seven days |
-| Task:MaxRunningTimeSpanInMinutes |    No| Maximum running time for treating request as timeout. Default: 60 minutes |
+| Task:MaxRunningTimeSpanInMinutes |    No| Maximum running time for treating request as time out. Default: 60 minutes |
 | HTTP_PROXY_BYPASS_URLS | No |    Specify URLs for bypassing proxy Example: HTTP_PROXY_BYPASS_URLS = abc.com, xyz.com |
 | AzureCognitiveServiceReadHost (Receipt, IdDocument Containers Only)| Yes    | Specify Read container uri Example:AzureCognitiveServiceReadHost=http://onprem-frread:5000 |
 | AzureCognitiveServiceLayoutHost (Document, Invoice Containers Only) |    Yes    | Specify Layout container uri Example:AzureCognitiveServiceLayoutHost=http://onprem-frlayout:5000 |
@@ -812,7 +1206,7 @@ That's it! In this article, you learned concepts and workflows for downloading,
 * The billing information must be specified when you instantiate a container.
 
 > [!IMPORTANT]
-> Azure AI containers are not licensed to run without being connected to Azure for metering. Customers need to enable the containers to communicate billing information with the metering service at all times. Azure AI containers do not send customer data (for example, the image or text that is being analyzed) to Microsoft.
+> Azure AI containers aren't licensed to run without being connected to Azure for metering. Customers need to enable the containers to always communicate billing information with the metering service. Azure AI containers don't send customer data (for example, the image or text that is being analyzed) to Microsoft.
 
 ## Next steps
 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: conceptual
-ms.date: 01/15/2025
+ms.date: 04/04/2025
 ms.author: lajanuar
 monikerRange: '<=doc-intel-4.0.0'
 ---
@@ -40,7 +40,6 @@ For Document Intelligence v4.0 `2024-11-30` (GA) supports page and line features
 
 * Angle, width/height, and unit aren't supported.
 * For each object detected, bounding polygon or bounding regions aren't supported.
-* Page range (`pages`) isn't supported as a parameter.
 * The `lines` object isn't supported.
 :::moniker-end
 
@@ -324,4 +323,4 @@ Generally, we recommended testing the workload and the workload patterns before
 ## Next steps
 
 > [!div class="nextstepaction"]
-> [Learn about error codes and troubleshooting](v3-error-guide.md)
\ No newline at end of file
+> [Learn about error codes and troubleshooting](v3-error-guide.md)

@@ -16,7 +16,7 @@ ms.custom: language-service-custom-classification
 After model training is completed, you can view your model details and see how well it performs against the test set. 
 
 > [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaulation is calcualted on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
+> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
 
 ## Prerequisites
 

@@ -42,7 +42,7 @@ When you're ready to start [using your model to make predictions](#how-do-i-use-
 
 ## What is the recommended CI/CD process?
 
-You can train multiple models on the same dataset within the same project. After you have trained your model successfully, you can [view its performance](how-to/view-model-evaluation.md). You can [deploy and test](quickstart.md#deploy-your-model) your model within [Language studio](https://aka.ms/languageStudio). You can add or remove labels from your data and train a **new** model and test it as well. View [service limits](service-limits.md)to learn about maximum number of trained models with the same project. When you [train a model](how-to/train-model.md), you can determine how your dataset is split into training and testing sets. You can also have your data split randomly into training and testing set where there is no guarantee that the reflected model evaluation is about the same test set, and the results are not comparable. It's recommended that you develop your own test set and use it to evaluate both models so you can measure improvement.
+You can train multiple models on the same dataset within the same project. After you have trained your model successfully, you can [view its performance](how-to/view-model-evaluation.md). You can [deploy and test](quickstart.md#deploy-your-model) your model within [Language studio](https://aka.ms/languageStudio). You can add or remove labels from your data and train a **new** model and test it as well. View [service limits](service-limits.md) to learn about maximum number of trained models with the same project. When you [train a model](how-to/train-model.md), you can determine how your dataset is split into training and testing sets. You can also have your data split randomly into training and testing set where there is no guarantee that the reflected model evaluation is about the same test set, and the results are not comparable. It's recommended that you develop your own test set and use it to evaluate both models so you can measure improvement.
 
 ## Does a low or high model score guarantee bad or good performance in production?
 

@@ -17,7 +17,7 @@ ms.custom: language-service-custom-ner
 After your model has finished training, you can view the model performance and see the extracted entities for the documents in the test set. 
 
 > [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from the data. To make sure that the evaulation is calcualted on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Test** documents when [labeling data](tag-data.md).
+> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from the data. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Test** documents when [labeling data](tag-data.md).
 
 ## Prerequisites
 

@@ -80,7 +80,7 @@ Use the following steps to label your data:
 7. In the bottom section of the right side pane you can add the current file you are viewing to the training set or the testing set. By default all the documents are added to your training set. Learn more about [training and testing sets](train-model.md#data-splitting) and how they are used for model training and evaluation.
 
     > [!TIP]
-    > If you are planning on using **Automatic** data spliting use the default option of assigning all the documents into your training set.
+    > If you are planning on using **Automatic** data splitting use the default option of assigning all the documents into your training set.
 
 8. Under the **Distribution** pivot you can view the distribution across training and testing sets. You have two options for viewing:
    * *Total instances* where you can view count of all labeled instances of a specific class.

@@ -11,4 +11,4 @@ ms.custom: include, ignite-2024
 ---
 
 > [!TIP]
-> You can use [**Azure AI Foundry**](../../../ai-foundry/what-is-ai-foundry.md) to try summarization without needing to write code. 
+> You can use [**Azure AI Foundry**](../../../ai-foundry/what-is-azure-ai-foundry.md) to try summarization without needing to write code. 

@@ -18,7 +18,7 @@ The key phrase extraction feature can evaluate unstructured text, and for each d
 This feature is useful if you need to quickly identify the main points in a collection of documents. For example, given input text "*The food was delicious and the staff was wonderful*", the service returns the main topics: "*food*" and "*wonderful staff*".
 
 > [!TIP]
-> If you want to start using this feature, you can follow the [quickstart article](../quickstart.md) to get started. You can also make requests using [Azure AI Foundry](../../../../ai-foundry//what-is-ai-foundry.md) without needing to write code.
+> If you want to start using this feature, you can follow the [quickstart article](../quickstart.md) to get started. You can also make requests using [Azure AI Foundry](../../../../ai-foundry//what-is-azure-ai-foundry.md) without needing to write code.
 
 ## Development options
 

@@ -12,6 +12,6 @@ To use key phrase extraction, you submit raw unstructured text for analysis and
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate key phrase extraction into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [key phrase extraction quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |

@@ -38,5 +38,5 @@ An AI system includes not only the technology, but also the people who use it, t
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Azure AI Foundry](../../../ai-foundry/what-is-ai-foundry.md) is a web-based platform that lets you use several Azure AI Language features without needing to write code.
+* [Azure AI Foundry](../../../ai-foundry/what-is-azure-ai-foundry.md) is a web-based platform that lets you use several Azure AI Language features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -12,6 +12,6 @@ To use language detection, you submit raw unstructured text for analysis and han
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate language detection into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [language detection quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |

@@ -44,5 +44,5 @@ An AI system includes not only the technology, but also the people who will use
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Azure AI Foundry](../../../ai-foundry/what-is-ai-foundry.md) is a web-based platform that lets you use several Language service features without needing to write code.
+* [Azure AI Foundry](../../../ai-foundry/what-is-azure-ai-foundry.md) is a web-based platform that lets you use several Language service features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -12,5 +12,5 @@ To use named entity recognition, you submit raw unstructured text for analysis a
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use named entity recognition with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use named entity recognition with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate named entity recognition into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [named entity recognition quickstart](../quickstart.md).        |

@@ -45,5 +45,5 @@ An AI system includes not only the technology, but also the people who use it, t
 ## Next steps
 
 There are two ways to get started using the Named Entity Recognition (NER) feature:
-* [Azure AI Foundry](../../../ai-foundry/what-is-ai-foundry.md) is a web-based platform that lets you use several Language service features without needing to write code.
+* [Azure AI Foundry](../../../ai-foundry/what-is-azure-ai-foundry.md) is a web-based platform that lets you use several Language service features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.  

@@ -16,7 +16,7 @@ ms.custom: language-service-custom-classification
 After model training is completed, you can view your model details and see how well it performs against the test set. Observing how well your model performed is called evaluation. The test set consists of data that wasn't introduced to the model during the training process.
 
 > [!NOTE]
-> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaulation is calcualted on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
+> Using the **Automatically split the testing set from training data** option may result in different model evaluation result every time you [train a new model](train-model.md), as the test set is selected randomly from your utterances. To make sure that the evaluation is calculated on the same test set every time you train a model, make sure to use the **Use a manual split of training and testing data** option when starting a training job and define your **Testing set** when [add your utterances](tag-utterances.md).
 
 
 ## Prerequisites

@@ -12,5 +12,5 @@ To use PII detection, you submit text for analysis and handle the API output in
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use personally identifying information detection with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use personally identifying information detection with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate PII detection into your applications using the REST API, or the client library available in various languages. For more information, see the [PII detection quickstart](../quickstart.md).        |

@@ -8,4 +8,4 @@ ms.custom: include, ignite-2024
 ---
 
 > [!TIP]
-> You can use [**Azure AI Foundry**](../../../../ai-foundry/what-is-ai-foundry.md) to try summarization without needing to write code. 
+> You can use [**Azure AI Foundry**](../../../../ai-foundry/what-is-azure-ai-foundry.md) to try summarization without needing to write code. 

@@ -190,7 +190,7 @@ Use this article to learn which natural languages are supported by the text PII,
 
 ## PII language support
 
-The Generally Available Conversational PII serivce currently supports English. Preview model version `2023-04-15-preview` supports English, German, Spanish, and French. 
+The Generally Available Conversational PII service currently supports English. Preview model version `2023-04-15-preview` supports English, German, Spanish, and French. 
 
 
 ---

@@ -126,5 +126,5 @@ An AI system includes not only the technology, but also the people who use it, t
 ## Next steps
 
 There are two ways to get started using the entity linking feature:
-* [Azure AI Foundry](../../../ai-foundry/what-is-ai-foundry.md) is a web-based platform that lets you use several Language service features without needing to write code.
+* [Azure AI Foundry](../../../ai-foundry/what-is-azure-ai-foundry.md) is a web-based platform that lets you use several Language service features without needing to write code.
 * The [quickstart article](quickstart.md) for instructions on making requests to the service using the REST API and client library SDK.

@@ -12,6 +12,6 @@ To use sentiment analysis, you submit raw unstructured text for analysis and han
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate sentiment analysis into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [sentiment analysis quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |
\ No newline at end of file

@@ -15,7 +15,7 @@ To use summarization, you submit for analysis and handle the API output in your
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate text summarization into your applications using the REST API, or the client library available in various languages. For more information, see the [summarization quickstart](../quickstart.md).        |
 
 
@@ -29,7 +29,7 @@ To use summarization, you submit for analysis and handle the API output in your
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate text summarization into your applications using the REST API, or the client library available in various languages. For more information, see the [summarization quickstart](../quickstart.md).  
 
 ---

@@ -11,4 +11,4 @@ ms.custom: include, build-2024, ignite-2024
 ---
 
 > [!TIP]
-> You can use [**Azure AI Foundry**](../../../../ai-foundry/what-is-ai-foundry.md) to try summarization without needing to write code. 
+> You can use [**Azure AI Foundry**](../../../../ai-foundry/what-is-azure-ai-foundry.md) to try summarization without needing to write code. 

@@ -13,6 +13,6 @@ To use Text Analytics for health, you submit raw unstructured text for analysis
 
 |Development option  |Description  |
 |---------|---------|
-|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-ai-foundry.md).         |
+|Azure AI Foundry     | Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the [Azure AI Foundry website](https://ai.azure.com) or [Azure AI Foundry documentation](../../../../ai-foundry/what-is-azure-ai-foundry.md).         |
 |REST API or Client library (Azure SDK)      | Integrate Text Analytics for health into your applications using the REST API, or the client library available in a variety of languages. For more information, see the [Text Analytics for health quickstart](../quickstart.md).        |
 | Docker container | Use the available Docker container to [deploy this feature on-premises](../how-to/use-containers.md). These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. |

@@ -21,7 +21,7 @@ This tutorial teaches you how to use Language in prompt flow utilizing [Azure AI
 
 - An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>.
 
-- You need an Azure AI Foundry hub or permissions to create one. Your user role must be **Azure AI Developer**, **Contributor**, or **Owner** on the hub. For more information, see [hubs](../../../ai-foundry/concepts/ai-resources.md) and [Azure AI roles](../../../ai-foundry/concepts/rbac-ai-foundry.md).
+- You need an Azure AI Foundry hub or permissions to create one. Your user role must be **Azure AI Developer**, **Contributor**, or **Owner** on the hub. For more information, see [hubs](../../../ai-foundry/concepts/ai-resources.md) and [Azure AI roles](../../../ai-foundry/concepts/rbac-azure-ai-foundry.md).
      - If your role is **Contributor** or **Owner**, you can [create a hub in this tutorial](#create-a-project-in-azure-ai-foundry-portal). 
      - If your role is **Azure AI Developer**, the hub must already be created.