Merge pull request #191769 from aahill/baher-update-2

Baher update - custom features

Author: Court72

articles/cognitive-services/language-service/conversational-language-understanding/how-to/deploy-query-model.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 01/26/2022
+ms.date: 03/15/2022
 ms.author: aahi
 ms.devlang: csharp, python
 ms.custom: language-service-clu, ignite-fall-2021
@@ -18,32 +18,57 @@ ms.custom: language-service-clu, ignite-fall-2021
 
 After you have [trained a model](./train-model.md) on your dataset, you're ready to deploy it. After deploying your model, you'll be able to query it for predictions. 
 
+> [!Tip]
+> Before deploying a model, make sure to view the model details to make sure that the model is performing as expected.
+
 ## Deploy model
 
-Deploying a model is to host it and make it available for predictions through an endpoint. You can only have 1 deployed model per project, deploying another one will overwrite the previously deployed model.
+Deploying a model hosts and makes it available for predictions through an endpoint.
 
 When a model is deployed, you will be able to test the model directly in the portal or by calling the API associated to it.
 
-Simply select a model and click on deploy model in the Deploy model page. 
+### Conversation projects deployments
+
+1.	Click on *Add deployment* to submit a new deployment job
+
+    :::image type="content" source="../media/add-deployment-model.png" alt-text="A screenshot showing the model deployment button in Language Studio." lightbox="../media/add-deployment-model.png":::
+
+2. In the window that appears, you can create a new deployment name by giving the deployment a name or override an existing deployment name. Then, you can add a trained model to this deployment name.
+
+    :::image type="content" source="../media/create-deployment-job.png" alt-text="A screenshot showing the add deployment job screen in Language Studio." lightbox="../media/create-deployment-job.png":::
+
+
+#### Swap deployments
 
-:::image type="content" source="../media/deploy-model.png" alt-text="A screenshot showing the model deployment page in Language Studio." lightbox="../media/deploy-model.png":::
+If you would like to swap the models between two deployments, simply select the two deployment names you want to swap and click on **Swap deployments**. From the window that appears, select the deployment name you want to swap with.
+
+:::image type="content" source="../media/swap-deployment.png" alt-text="A screenshot showing a swapped deployment in Language Studio." lightbox="../media/swap-deployment.png":::
+
+#### Delete deployment
+
+To delete a deployment, select the deployment you want to delete and click on **Delete deployment**.
 
 > [!TIP]
 > If you're using the REST API, see the [quickstart](../quickstart.md?pivots=rest-api#deploy-your-model) and REST API [reference documentation](https://westus2.dev.cognitive.microsoft.com/docs/services/language-authoring-clu-apis-2021-11-01-preview/operations/Deployments_TriggerDeploymentJob) for examples and more information.
 
-**Orchestration workflow projects deployments**
+> [!NOTE]
+> You can only have ten deployment names.
+
+### Orchestration workflow projects deployments
+
+1.	Click on **Add deployment** to submit a new deployment job.
+
+    Like conversation projects, In the window that appears, you can create a new deployment name by giving the deployment a name or override an existing deployment name. Then, you can add a trained model to this deployment name and press next.
 
-When you're deploying an orchestration workflow project, A small window will show up for you to confirm your deployment, and configure parameters for connected services.
+    :::image type="content" source="../media/create-deployment-job-orch.png" alt-text="A screenshot showing deployment job creation in Language Studio." lightbox="../media/create-deployment-job-orch.png":::
 
-If you're connecting one or more LUIS applications, specify the deployment name, and whether you're using *slot* or *version* type deployment.       
-* The *slot* deployment type requires you to pick between a production and staging slot.
-* The *version* deployment type requires you to specify the version you have published.
+2. If you're connecting one or more LUIS applications or conversational language understanding projects, specify the deployment name.
 
-No configurations are required for custom question answering and conversational language understanding connections, or unlinked intents.
+    No configurations are required for custom question answering or unlinked intents.
 
-LUIS projects **must be published** to the slot configured during the Orchestration deployment, and custom question answering KBs must also be published to their Production slots.
+    LUIS projects **must be published** to the slot configured during the Orchestration deployment, and custom question answering KBs must also be published to their Production slots.
 
-:::image type="content" source="../media/deploy-connected-services.png" alt-text="A screenshot showing the deployment screen for orchestration workflow projects." lightbox="../media/deploy-connected-services.png":::
+    :::image type="content" source="../media/deploy-connected-services.png" alt-text="A screenshot showing the deployment screen for orchestration workflow projects." lightbox="../media/deploy-connected-services.png":::
 
 ## Send a Conversational Language Understanding request
 

articles/cognitive-services/language-service/conversational-language-understanding/includes/quickstarts/language-studio.md

@@ -5,7 +5,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: include
-ms.date: 01/27/2022
+ms.date: 03/15/2022
 ms.author: aahi
 ms.custom: ignite-fall-2021
 ---
@@ -29,7 +29,7 @@ ms.custom: ignite-fall-2021
 
 Once you have a Language resource associated with your account, create a Conversational Language Understanding project. In this quickstart, you'll create a project that can identify commands for email, such as: reading emails by certain people, deleting emails, and attaching a document to an email.
 
-1. In [Language Studio](https://aka.ms/languageStudio), find the section labelled **Understand conversational language** and select **Conversational language understanding**.  
+1. In [Language Studio](https://aka.ms/languageStudio), find the section named **Understand conversational language** and select **Conversational language understanding**.  
     :::image type="content" source="../../media/select-custom-clu.png" alt-text="A screenshot showing the location of Custom Language Understanding in the Language Studio landing page." lightbox="../../media/select-custom-clu.png"::: 
 
 
@@ -99,15 +99,18 @@ When you're done, select **Save Changes** to save the utterances and labels to t
 
 ## Train your model and view its details
 
-Select **train model** on the left of the screen. Select **Start a training job**. To train your model, you need to provide a name for the model. Write a name like "*v1*" and press the enter key. 
+Select **train model** on the left of the screen. Select **Start a training job**. To train your model, you need to provide a name for the model. Write a name like "*v1*" and press the enter key.
 
-Turn off **Run evaluation with training** before selecting **Train**. 
+> [!NOTE]
+> If you did not [tag utterances](#tag-utterances) you will only be allowed to train using the **Automatically split the testing set from all data** option. See [Add utterances to testing set](../../how-to/tag-utterances.md#tag-utterances) for more information.
 
-You should see the **View model details** page. Wait until training completes, which may take about 5 minutes. When training succeeds, Select **Deploy Model** on the left of the screen.
+When the training job is complete, which may take some time, you should see the output model performance in the **View model details** page.
 
 ## Deploy your model
 
-From the **Deploy model** page on the left of the screen, select the trained model and select the **Deploy model** button. In the screen that appears, select **Deploy**.
+From the **Deploy model** page on the left of the screen, select **Add deployment**.
+
+In the window that appears, give your deployment a **deployment name** and then assign your trained model to this deployment name and then select **Submit**.
 
 ## Test your model
 

articles/cognitive-services/language-service/conversational-language-understanding/media/add-deployment-model.png

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: how-to
-ms.date: 01/07/2022
+ms.date: 03/15/2022
 ms.author: aahi
 ms.custom: language-service-custom-classification, ignite-fall-2021
 ---
@@ -29,11 +29,20 @@ See the [application development lifecycle](../overview.md#project-development-l
 
 ## Deploy your model
 
-After your model is [trained](train-model.md), you can deploy it. Deploying your model lets you start using it to classify text. You can deploy your model using the [REST API](https://westus2.dev.cognitive.microsoft.com/docs/services/language-authoring-apis-2021-11-01-preview/operations/Deployments_TriggerDeploymentJob) or Language Studio. To use Language Studio, see the steps below:
+Deploying a model hosts it and makes it available for predictions through an endpoint.
+
+
+
+When a model is deployed, you will be able to test the model directly in the portal or by calling the API associated with it.
+
+> [!NOTE]
+> You can only have ten deployment names.
 
 [!INCLUDE [Deploy a model using Language Studio](../includes/deploy-model-language-studio.md)]
+   
+### Delete deployment
 
-If you deploy your model through the Language Studio, your `deployment-name` is `prod`.
+To delete a deployment, select the deployment you want to delete and click **Delete deployment**
 
 > [!TIP]
 > You can [test your model in Language Studio](../quickstart.md?pivots=language-studio#test-your-model) by sending samples of text for it to classify. 
@@ -58,7 +67,7 @@ If you deploy your model through the Language Studio, your `deployment-name` is
 
 5. In the response header you receive extract `jobId` from `operation-location`, which has the format: `{YOUR-ENDPOINT}/text/analytics/v3.2-preview.2/analyze/jobs/<jobId}>`
 
-6. Copy the retrieve request and replace `<OPERATION-ID>` with `jobId` received form last step and submit the request.
+6. Copy the retrieve request and replace `<OPERATION-ID>` with `jobId` received from the last step and submit the request.
 
     :::image type="content" source="../media/get-prediction-url-3.png" alt-text="run-inference-3" lightbox="../media/get-prediction-url-3.png":::
 

articles/cognitive-services/language-service/conversational-language-understanding/media/create-deployment-job-orch.png

@@ -4,12 +4,16 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: language-service
 ms.topic: include
-ms.date: 02/28/2022
+ms.date: 03/15/2022
 ms.author: aahi
 ---
 
-1. Go to your project in [Language Studio](https://aka.ms/custom-classification)
+1. Go to your project in [Language studio](https://aka.ms/custom-classification).
+2. From the left panel, select **Deploy model**.
+3. Click on **Add deployment** to submit a new deployment job.
 
-2. Select **Deploy model** from the left side menu.
+    :::image type="content" source="../media/deploy-model.png" alt-text="A screenshot showing the deployment button." lightbox="../media/deploy-model.png":::
 
-3. Select the model you want to deploy and from the top menu click on **Deploy model**.
+4. In the window that appears, you can create a new deployment name by or override an existing one. Then, you can add a trained model to this deployment name.
+
+   :::image type="content" source="../media/add-deployment.png" alt-text="A screenshot showing the screen for a new deployment" lightbox="../media/add-deployment.png":::