@@ -22,7 +22,11 @@ You can query the deployment programmatically [Prediction API](/rest/api/languag
 
 First you need to get your resource key and endpoint:
 
-[!INCLUDE [Get keys and endpoint Azure portal](../../includes/key-endpoint-page-azure-portal.md)]
+1. Go to your resource overview page in the [Azure portal](https://portal.azure.com/#home)
+
+1. From the menu on the left side, select **Keys and Endpoint**. You use the endpoint and key for the API requests.
+
+    :::image type="content" source="../media/key-endpoint-page.png" alt-text="A screenshot showing the key and endpoint page in the Azure portal." lightbox="../media/key-endpoint-page.png":::
 
 ### Submit a custom text classification task
 

@@ -1,33 +0,0 @@
----
-title: Create an Azure Language in Foundry Tools resource
-titleSuffix: Foundry Tools
-description: Learn how to create an Azure Language in Foundry Tools resource.
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-## Create an Azure Language in Foundry Tools resource
-
-1. Sign in to the [Azure portal](https://portal.azure.com).
-1. Select **Create a resource**, and then go to **AI + Machine Learning** > **Language**.
-   Or, go to [Create a Language resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics).
-1. Enter all the required settings:
-
-    |Setting|Value|
-    |--|--|
-    |Name|Enter a name (2-64 characters).|
-    |Subscription|Select the appropriate subscription.|
-    |Location|Select a nearby location.|
-    |Pricing tier| Enter **S**, the standard pricing tier.|
-    |Resource group|Select an available resource group.|
-
-1. Select **Create**, and wait for the resource to be created. Your browser automatically redirects to the newly created resource page.
-1. Collect the configured `endpoint` and an API key:
-
-    |Resource tab in portal|Setting|Value|
-    |--|--|--|
-    |**Overview**|Endpoint|Copy the endpoint. It appears similar to `https://my-resource.cognitiveservices.azure.com/text/analytics/v3.0`.|
-    |**Keys**|API Key|Copy one of the two keys. It's a 32-character alphanumeric string with no spaces or dashes: <`xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`>.|

@@ -1,15 +0,0 @@
----
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-To delete your model from within the [Language Studio](https://aka.ms/LanguageStudio):
-
-1. Select **Model performance** from the left side menu.
-
-2. Select the **model name** you want to delete and select **Delete** from the top menu.
-
-3. In the window that appears, select **OK** to delete the model. 

@@ -1,34 +0,0 @@
----
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-Create a **DELETE** request using the following URL, headers, and JSON body to delete a trained model.
-
-
-### Request URL
-
-```rest
-{Endpoint}/language/authoring/analyze-text/projects/{PROJECT-NAME}/models/{trainedModelLabel}?api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `myProject` |
-|`{trainedModelLabel}`     | The name for your model name. This value is case-sensitive.   | `model1` |
-|`{API-VERSION}`     | The version of the API you're calling. The value referenced is for the latest version released. See [Model lifecycle](../../concepts/model-lifecycle.md) to learn more about other available API versions.  | `2022-05-01` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-
-Once you send your API request, you receive a `204` response indicating success, which means your trained model is deleted.

@@ -1,30 +0,0 @@
----
-titleSuffix: Foundry Tools
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-1. After the deployment job is completed successfully, select the deployment you want to use and from the top menu select **Get prediction URL**.
-
-    :::image type="content" source="../../media/custom/get-prediction-url-1.png" alt-text="A screenshot showing the button to get a prediction URL." lightbox="../../media/custom/get-prediction-url-1.png":::
-
-2. In the window that appears, under the **Submit** pivot, copy the sample request URL and body into your command line.
-
-3. Replace `<YOUR_DOCUMENT_HERE>` with the actual text you want to extract entities from.
-
-    :::image type="content" source="../../media/custom/get-prediction-url-2.png" alt-text="A screenshot showing the example request." lightbox="../../media/custom/get-prediction-url-2.png":::
-
-4. Submit the `POST` cURL request in your terminal or command prompt. You receive a 202 response with the API results if the request was successful.
-
-5. In the response header you receive extract `{JOB-ID}` from `operation-location`, which has the format: `{ENDPOINT}/text/analytics/v3.2-preview.2/analyze/jobs/<JOB-ID}>`
-
-6. Back to Language Studio; select **Retrieve** pivot from the same window you got the example request you got earlier and copy the sample request into a text editor. 
-
-    :::image type="content" source="../../media/custom/get-prediction-url-3.png" alt-text="A screenshot showing the example retrieval request." lightbox="../../media/custom/get-prediction-url-3.png":::
-
-7. Replace `<JOB-ID>` with the `{JOB-ID}` you extracted from the previous step. 
-
-8. Submit the `GET` cURL request in your terminal or command prompt.

@@ -1,23 +0,0 @@
----
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-To **load** your model data:
-
-1. Select any model in the **model evaluation** page.
-
-2. Select the **Load model data** button. 
-
-3. Confirm that you do not have any unsaved changes you need to capture in window that appears, and select **Load data**. 
-
-4. Wait until your model data finishes loading back into your project. On completion, you'll be redirected back to the **Schema design** page. 
-
-To **export** your model data:
-
-1. Select any model in the **model evaluation** page.
-
-2.  Select the **Export model data** button. Wait for the JSON snapshot of your model to be downloaded locally. 

@@ -1,80 +0,0 @@
----
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-### Load model data 
-
-Create a **POST** request using the following URL, headers, and JSON body to load your model data to your project.
-
-### Request URL
-
-Use the following URL when creating your API request. Replace the placeholder values with your own values. 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/models/{MODEL-NAME}:load-snapshot?stringIndexType=Utf16CodeUnit&api-version={API-VERSION}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `EmailApp` |
-|`{API-VERSION}`     | The version of the API you're calling. | `2022-10-01-preview` |
-|`{MODEL-NAME}`       | The name of your model. This value is case-sensitive. | `v1` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-
-Once you send your API request, you receive a `202` response indicating success. In the response headers, extract the `operation-location` value formatted like this: 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/models/{MODEL-NAME}/jobs/{JOB-ID}?api-version={API-VERSION}
-``` 
-
-`JOB-ID` is used to identify your request, since this operation is asynchronous. Use this URL to get the status of your model data loading, using the same authentication method.
-
-
-### Export model data
-
-Create a **POST** request using the following URL, headers, and JSON body to export your model data.
-
-### Request URL
-
-Use the following URL when creating your API request. Replace the placeholder values with your own values. 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/:export?stringIndexType=Utf16CodeUnit&api-version={API-VERSION}&trainedModelLabel={MODEL-NAME}
-```
-
-|Placeholder  |Value  | Example |
-|---------|---------|---------|
-|`{ENDPOINT}`     | The endpoint for authenticating your API request.   | `https://<your-custom-subdomain>.cognitiveservices.azure.com` |
-|`{PROJECT-NAME}`     | The name for your project. This value is case-sensitive.   | `EmailApp` |
-|`{API-VERSION}`     | The version of the API you're calling. | `2022-10-01-preview` |
-|`{MODEL-NAME}`       | The name of your model. This value is case-sensitive. | `v1` |
-
-### Headers
-
-Use the following header to authenticate your request. 
-
-|Key|Value|
-|--|--|
-|`Ocp-Apim-Subscription-Key`| The key to your resource. Used for authenticating your API requests.|
-
-
-Once you send your API request, you receive a `202` response indicating success. In the response headers, extract the `operation-location` value formatted like this: 
-
-```rest
-{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/jobs/{JOB-ID}?api-version={API-VERSION}
-``` 
-
-`JOB-ID` is used to identify your request, since this operation is asynchronous. Use this URL to get the exported project JSON, using the same authentication method.

@@ -1,80 +0,0 @@
----
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-1. Go to your project page in [Language Studio](https://aka.ms/languageStudio).
-
-2. Select **Model performance** from the menu on the left side of the screen.
-
-3. On this page, you can only view the successfully trained models, F1 score for each model and [model expiration date](../../concepts/model-lifecycle.md). You can select on the model name for more details about its performance.
-
-> [!NOTE]
-> * Entities that aren't labeled or predicted in the test set will not be part of the displayed results.
-
-### [Overview](#tab/overview)
-
-* In this tab you can view the model's details such as: F1 score, precision, recall, date and time for the training job, total training time and number of training and testing documents included in this training job.  
-
-    :::image type="content" source="../../media/custom/overview.png" alt-text="A screenshot that shows the overview page for model evaluation." lightbox="../../media/custom/overview.png":::
-
-* You'll also see guidance on how to improve the model. When you select *view details*,  a side panel will open to give more guidance on how to improve the model. In this example, *BorrowerAddress* and *BorrowerName* entities are confused with *$none* entity. Selecting the confused entities opens the data labeling page to label more data with the correct entity.
-
-    :::image type="content" source="../../media/custom/overview-guidance.png" alt-text="A screenshot that shows the guidance page for model evaluation." lightbox="../../media/custom/overview-guidance.png":::
-    
-### [Entity type performance](#tab/entity-performance)
-
-* This is a snapshot of how your model performed during testing. The metrics here are static and tied to your model, so they won’t update until you train again.
-
-* You can see for each entity, precision, recall, F1 score, number of training and testing labels.
-
-
-    :::image type="content" source="../../media/custom/entity-type-performace.png" alt-text="A screenshot of entity performance." lightbox="../../media/custom/entity-type-performace.png":::
-
-### [Test set details](#tab/test-set)
-
-* Here you can see the documents included in the **test set** and the result entity type for each document. You can use the *Show mismatches only* toggle to show only documents with mismatches, or unselect the toggle to view all document in the test set.
-
-* For each document, you can view: labeled text, its respective labeled entity type and what entity it was predicted with. Also, you can see whether it's a true positive, false positive or false negative. 
-
-    :::image type="content" source="../../media/custom/test-set.png" alt-text="A screenshot of test set details." lightbox="../../media/custom/test-set.png":::
-    
-
-### [Dataset distribution](#tab/dataset-distribution) 
-
-This snapshot shows how entities are distributed across your training and testing sets. This data is static and tied to your model, so it won’t update until you train again.
-
-* You can view the dataset distribution in *graph* or *table* view.
-
-**Graph view**
-
-* *Documents with at least one label*: This view shows for each entity, the number of occurrences for this entity across the training and testing sets.
-
-* *Total instances throughout documents*: This view shows for each entity, the labeled occurrences across training and testing sets.
-
-  :::image type="content" source="../../media/custom/dataset-graph.png" alt-text="A screenshot showing distribution in graph view." lightbox="../../media/custom/dataset-graph.png":::
-
-**Table view**
-
-For each *entity*, you can view: tags per entity in training set, tagged documents in training set, tags per entity in testing set, tagged documents in testing set, tags per entity total and tagged documents total.
-
-  :::image type="content" source="../../media/custom/dataset-table.png" alt-text="A screenshot showing distribution in table view." lightbox="../../media/custom/dataset-table.png":::
-
-### [Confusion matrix](#tab/confusion-matrix) 
-
-A confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target entities. The matrix compares the actual target values with those values predicted by the machine learning model to show how well the extraction model is performing and what kinds of errors it's making.
-
-You can view the confusion matrix in *normalized* or *raw count* view.
-
-  :::image type="content" source="../../media/custom/confusion.png" alt-text="A screenshot of a confusion matrix in Language Studio." lightbox="../../media/custom/confusion.png":::
-
-* All values: Shows the confusion matrix for all entities.
-
-* Only errors: Shows the confusion matrix for entities with errors only.
-
-* Only matches: Shows the confusion matrix for entities with correct predictions only.
-
----

@@ -1,20 +0,0 @@
----
-titleSuffix: Foundry Tools
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-1. Go to your **project settings** page in [Language Studio](https://aka.ms/languageStudio).
-
-2. You can see project details.
-
-3. On this page, you can update project description and enable/disable Multi-lingual dataset in project settings.
-
-4. You can also view the connected storage account and container to your Language resource.
-
-5. You can also retrieve your primary resource key from this page.
-
-    :::image type="content" source="../../media/project-details.png" alt-text="A screenshot of the project settings page in Language Studio." lightbox="../../media/project-details.png":::

@@ -1,28 +0,0 @@
----
-titleSuffix: Foundry Tools
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-### Create a new Language resource from Language Studio
-
-If it's your first time logging in, you'll see a window in [Language Studio](https://aka.ms/languageStudio) that lets you choose an existing Language resource or create a new one. You can also create a resource by clicking the settings icon in the top-right corner, selecting **Resources**, then clicking **Create a new resource**.
-
-Create a Language resource with following details.
-
-|Instance detail  |Required value  |
-|---------|---------|
-|Azure subscription| **Your Azure subscription**|
-|Azure resource group| **Your Azure resource group**|
-|Azure resource name| **Your Azure resource name**|
-|Location | The region      |
-|Pricing tier     | The pricing tier of your Language resource.        |
-
-> [!IMPORTANT]
-> * Make sure to enable **Managed Identity** when you create a Language resource. 
-> * Read and confirm Responsible AI notice
-
-To use this service, you'll need to [create an Azure storage account](/azure/storage/common/storage-account-create) if you don't have one already. 

@@ -6,7 +6,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: include
-ms.date: 11/18/2025
+ms.date: 02/23/2026
 ms.author: lajanuar
 ---
 Use the following steps to set the required roles for your Language resource and storage account.
@@ -19,7 +19,7 @@ Use the following steps to set the required roles for your Language resource and
 2. Select **Access Control (IAM)** in the left pane.
 3. Select **Add** to **Add Role Assignments**, and choose the appropriate role for your account.
 
-    You should have the **owner** or **contributor** role assigned on your Language resource.
+    * You should have the **owner** or **contributor** role assigned on your Language resource.
 
 4. Within **Assign access to**, select **User, group, or service principal**
 5. Select **Select members**

@@ -5,46 +5,34 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: include
-ms.date: 11/18/2025
+ms.date: 02/23/2026
 ms.author: lajanuar
 ---
-|Requirement  |Description  |
-|---------|---------|
-|Regions     |  If you don't have a resource, you need to create a new one in a supported region.        |
-|Pricing tier     | pricing tier for your resource.       |
-|Managed identity     | Make sure that the resource's managed identity setting is enabled. Otherwise, read the next section. |
+| Requirement | Description |
+| --- | --- |
+| Regions | If you don't have a resource, you need to create a new one in a supported region. |
+| Pricing tier | pricing tier for your resource. |
+| Managed identity | Make sure that the resource's managed identity setting is enabled. Otherwise, read the next section. |
 
-To use this service, you'll need to [create an Azure storage account](/azure/storage/common/storage-account-create) if you don't have one already. 
+To use this service, you'll need to [create an Azure storage account](/azure/storage/common/storage-account-create) if you don't have one already.
 
-## Enable identity management for your resource
-
-# [Azure portal](#tab/azure-portal)
+## Enable identity management using Azure portal
 
 Your Language resource must have identity management, to enable it using [Azure portal](https://portal.azure.com/):
 
 1. Go to your Language resource
-2. From left hand menu, under **Resource Management** section, select **Identity**
-3. From **System assigned** tab, make sure to set **Status** to **On**
-
-# [Language Studio](#tab/language-studio)
-
-Your Language resource must have identity management, to enable it using [Language Studio](https://aka.ms/languageStudio):
-
-1. Select the settings icon in the top right corner of the screen
-2. Select **Resources**
-3. Select the check box **Managed Identity** for your Azure Language in Foundry Tools resource.
-
----
+1. From left hand menu, under **Resource Management** section, select **Identity**
+1. From **System assigned** tab, make sure to set **Status** to **On**
 
 ### Enable the custom feature for your resource
 
 Make sure to enable this service's custom feature from Azure portal.
 
 1. Go to your Language resource in [Azure portal](https://portal.azure.com/)
-2. From the left side menu, under **Resource Management** section, select **Features**
-3. Enable this service's custom feature
-4. Connect your storage account
-5. Select **Apply**
+1. From the left side menu, under **Resource Management** section, select **Features**
+1. Enable this service's custom feature
+1. Connect your storage account
+1. Select **Apply**
 
 > [!IMPORTANT]
 > Make sure that your **Language resource** has **storage blob data contributor** role assigned on the storage account you're connecting.
@@ -55,7 +43,7 @@ Make sure to enable this service's custom feature from Azure portal.
 
 ### Enable CORS for your storage account
 
-Make sure to allow (**GET, PUT, DELETE**) methods when enabling Cross-Origin Resource Sharing (CORS). 
+Make sure to allow (**GET, PUT, DELETE**) methods when enabling Cross-Origin Resource Sharing (CORS).
 Set allowed origins field to `https://language.cognitive.azure.com`. Allow all header by adding `*` to the allowed header values, and set the maximum age to `500`.
 
 :::image type="content" source="../../custom-named-entity-recognition/media/cors.png" alt-text="A screenshot showing how to use CORS for storage accounts." lightbox="../../custom-named-entity-recognition/media/cors.png":::

@@ -1,13 +0,0 @@
----
-author: laujan
-manager: nitinme
-ms.service: azure-ai-language
-ms.topic: include
-ms.date: 11/18/2025
-ms.author: lajanuar
----
-1. Go to your resource overview page in the [Azure portal](https://portal.azure.com/#home)
-
-2. From the menu on the left side, select **Keys and Endpoint**. You use the endpoint and key for the API requests 
-
-    :::image type="content" source="../media/key-endpoint-page.png" alt-text="A screenshot showing the key and endpoint page in the Azure portal." lightbox="../media/key-endpoint-page.png":::

@@ -1,19 +0,0 @@
----
- title: include file
- description: include file
- #services: cognitive-services
- author: laujan
- ms.service: azure-ai-language
- ms.topic: include
- ms.date: 11/05/2025
- ms.author: lajanuar
- ms.custom: include
----
-This table shows the relationship between SDK versions and supported API versions of the service:
-
-|SDK version  |Supported API version of service  |
-|---------|---------|
-|5.2-beta.x     | 3.0, 3.1, 3.2-preview.x (default)       |
-|5.1.X     | 3.0, 3.1 (default)       |
-|5.0.0    | 3.0       |
-|1.0.X    | 3.0      |

@@ -102,11 +102,11 @@ Chit-chat is supported for several predefined personalities:
 
 |Personality |Custom question answering dataset file |
 |---------|-----|
-|Professional |[qna_chitchat_professional.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_professional.tsv) |
-|Friendly |[qna_chitchat_friendly.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_friendly.tsv) |
-|Witty |[qna_chitchat_witty.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_witty.tsv) |
-|Caring |[qna_chitchat_caring.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_caring.tsv) |
-|Enthusiastic |[qna_chitchat_enthusiastic.tsv](https://qnamakerstore.blob.core.windows.net/qnamakerdata/editorial/qna_chitchat_enthusiastic.tsv) |
+|Professional | qna_chitchat_professional.tsv |
+|Friendly | qna_chitchat_friendly.tsv |
+|Witty | qna_chitchat_witty.tsv |
+|Caring | qna_chitchat_caring.tsv |
+|Enthusiastic | qna_chitchat_enthusiastic.tsv |
 
 The responses range from formal to informal and irreverent. You should select the personality that is closest aligned with the tone you want for your bot. You can view the datasets, and choose one that serves as a base for your bot, and then customize the responses.
 

@@ -43,7 +43,7 @@ To follow is an example of a manual with an index page, and hierarchical content
 
 ### Brochures, guidelines, papers, and other files
 
-Many other types of documents can also be processed to generate question answer pairs, provided they have a clear structure and layout. These documents include: Brochures, guidelines, reports, white papers, scientific papers, policies, books, etc. See an example [here](https://qnamakerstore.blob.core.windows.net/qnamakerdata/docs/Manage%20Azure%20Blob%20Storage.docx).
+Many other types of documents can also be processed to generate question answer pairs, provided they have a clear structure and layout. These documents include: Brochures, guidelines, reports, white papers, scientific papers, policies, books, etc.
 
 To follow is an example of a semi-structured doc, without an index:
 

@@ -10,6 +10,7 @@ ms.date: 11/18/2025
 ms.author: lajanuar
 ms.custom: cogserv-non-critical-language
 ---
+
 # Deploy a key phrase extraction container to Azure Kubernetes Service
 
 Learn how to deploy a [key phrase extraction Docker container](../key-phrase-extraction/how-to/use-containers.md) image to Azure Kubernetes Service (AKS). This procedure shows how to create a Language resource, how to associate a container image, and how to exercise this orchestration of the two from a browser. Using containers can shift your attention away from managing infrastructure to instead focusing on application development. While this article uses the key phrase extraction container as an example, you can use this process for other containers offered by Azure Language in Foundry Tools
@@ -23,13 +24,33 @@ This procedure requires several tools that must be installed and run locally. Do
 * The [Azure CLI](/cli/azure/install-azure-cli) installed.
 * The [Kubernetes CLI](https://kubernetes.io/docs/tasks/tools/install-kubectl/) installed.
 * An Azure resource with the correct pricing tier. Not all pricing tiers work with this container:
-    * **Language** resource with F0 or standard pricing tiers only.
-    * **Foundry Tools** resource with the S0 pricing tier.
+  * **Language** resource with F0 or standard pricing tiers only.
+  * **Foundry Tools** resource with the S0 pricing tier.
 
-[!INCLUDE [Create a Language resource](../includes/containers/create-text-analytics-resource.md)]
+## Create an Azure Language in Foundry Tools resource
 
-[!INCLUDE [Create a language container on Azure Kubernetes Service (AKS)](../../containers/includes/create-aks-resource.md)]
+1. Sign in to the [Azure portal](https://portal.azure.com).
+1. Select **Create a resource**, and then go to **AI + Machine Learning** > **Language**.
+   Or, go to [Create a Language resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics).
+1. Enter all the required settings:
+
+    |Setting|Value|
+    |--|--|
+    |Name|Enter a name (2-64 characters).|
+    |Subscription|Select the appropriate subscription.|
+    |Location|Select a nearby location.|
+    |Pricing tier| Enter **S**, the standard pricing tier.|
+    |Resource group|Select an available resource group.|
 
+1. Select **Create**, and wait for the resource to be created. Your browser automatically redirects to the newly created resource page.
+1. Collect the configured `endpoint` and an API key:
+
+    |Resource tab in portal|Setting|Value|
+    |--|--|--|
+    |**Overview**|Endpoint|Copy the endpoint. It appears similar to `https://my-resource.cognitiveservices.azure.com/text/analytics/v3.0`.|
+    |**Keys**|API Key|Copy one of the two keys. It's a 32-character alphanumeric string with no spaces or dashes: <`xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx`>.|
+
+[!INCLUDE [Create a language container on Azure Kubernetes Service (AKS)](../../containers/includes/create-aks-resource.md)]
 
 ## Deploy the Key Phrase Extraction container to an AKS cluster