Merge pull request #201575 from BaherAbdullah/patch-157

Update quickstart.md

Author: jborsecnik

articles/cognitive-services/language-service/concepts/model-lifecycle.md

@@ -101,10 +101,10 @@ Use the table below to find which API versions are supported by each feature:
 
 | Feature                                             | Supported versions                                                  | Latest Generally Available version | Latest preview version |
 |-----------------------------------------------------|---------------------------------------------------------------------|------------------------------------|------------------------|
-| Custom text classification                  | `2022-05-01`    ,`2022-05-15-preview`	                         |      `2022-05-01`            |  `2022-05-15-preview`	            |
-| Conversational language understanding       | `2022-05-01`   ,`2022-05-15-preview`	                          |      `2022-05-01`            |    `2022-05-15-preview`	            |
-| Custom named entity recognition             | `2022-05-01`  ,`2022-05-15-preview`                           |      `2022-05-01`            |     `2022-05-15-preview`	           |
-| Orchestration workflow                      | `2022-05-01`,`2022-05-15-preview`                            |      `2022-05-01`            |   `2022-05-15-preview`	             |
+| Custom text classification                  | `2022-05-01`                            |      `2022-05-01`            |            |
+| Conversational language understanding       | `2022-05-01`                           |      `2022-05-01`            |             |
+| Custom named entity recognition             | `2022-05-01`                          |      `2022-05-01`            |              |
+| Orchestration workflow                      | `2022-05-01`                            |      `2022-05-01`            |           |
 
 
 ## Next steps

articles/cognitive-services/language-service/conversational-language-understanding/concepts/data-formats.md

@@ -88,7 +88,7 @@ If you're [importing a project](../how-to/create-project.md#import-project) into
 
 |Key  |Placeholder  |Value  | Example |
 |---------|---------|----------|--|
-| `api-version` | `{API-VERSION}`     | The version of the API you're calling. The value referenced here is for the latest released [model version](../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) released.  | `2022-03-01-preview` |
+| `api-version` | `{API-VERSION}`     | The version of the API you're calling. The value referenced here is for the latest released [model version](../../concepts/model-lifecycle.md#choose-the-model-version-used-on-your-data) released.  | `2022-05-01` |
 |`confidenceThreshold`|`{CONFIDENCE-THRESHOLD}`|This is the threshold score below which the intent will be predicted as [none intent](none-intent.md)|`0.7`|
 | `projectName` | `{PROJECT-NAME}` | The name of your project. This value is case-sensitive. | `EmailApp` |
 | `multilingual` | `true`| A boolean value that enables you to have documents in multiple languages in your dataset and when your model is deployed you can query the model in any supported language (not necessarily included in your training documents. See [Language support](../language-support.md#multi-lingual-option) for more information about supported language codes.  | `true`|

articles/cognitive-services/language-service/conversational-language-understanding/how-to/call-api.md

@@ -44,10 +44,6 @@ First you will need to get your resource key and endpoint:
 
 # [Client libraries (Azure SDK)](#tab/azure-sdk)
 
-First you will need to get your resource key and endpoint:
-
-[!INCLUDE [Get keys and endpoint Azure Portal](../includes/get-keys-endpoint-azure.md)]
-
 ### Use the client libraries (Azure SDK)
 
 You can also use the client libraries provided by the Azure SDK to send requests to your model. 

articles/cognitive-services/language-service/conversational-language-understanding/how-to/create-project.md

@@ -105,7 +105,7 @@ You can export a Conversational Language Understanding project as a JSON file at
 
 ---
 
-## Delete resources 
+## Delete project 
 
 ### [Language Studio](#tab/language-studio)
 

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

@@ -15,19 +15,19 @@ ms.custom: language-service-clu,
 
 # Deploy a model 
 
-Once you are satisfied with how your model performs, it's ready to be deployed, and query it for predictions from utterances. Deploying a model makes it available for use through the [prediction API](https://aka.ms/ct-runtime-swagger).
+Once you are satisfied with how your model performs, it's ready to be deployed, and query it for predictions from utterances. Deploying a model makes it available for use through the [prediction API](https://aka.ms/clu-runtime-api).
 
 ## Prerequisites
 
 * A successfully [created project](create-project.md) 
 * [Labeled utterances](tag-utterances.md) and successfully [trained model](train-model.md)
-* Reviewed the [model evaluation details](view-model-evaluation.md) to determine how your model is performing.
+* Reviewed the [model performance](view-model-evaluation.md) to determine how your model is performing.
 
 See [project development lifecycle](../overview.md#project-development-lifecycle) for more information.
 
 ## Deploy model
 
-After you have reviewed the model's performance and decide it's fit to be used in your environment, you need to assign it to a deployment to be able to query it. Assigning the model to a deployment makes it available for use through the [prediction API](https://aka.ms/clu-apis). It is recommended to create a deployment named `production` to which you assign the best model you have built so far and use it in your system. You can create another deployment called `staging` to which you can assign the model you're currently working on to be able to test it. You can have a maximum on 10 deployments in your project. 
+After you have reviewed the model's performance and decide it's fit to be used in your environment, you need to assign it to a deployment to be able to query it. Assigning the model to a deployment makes it available for use through the [prediction API](https://aka.ms/clu-runtime-api). It is recommended to create a deployment named `production` to which you assign the best model you have built so far and use it in your system. You can create another deployment called `staging` to which you can assign the model you're currently working on to be able to test it. You can have a maximum on 10 deployments in your project. 
 
 # [Language Studio](#tab/language-studio)
 

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

@@ -21,7 +21,7 @@ To deploy your model from within the [Language Studio](https://aka.ms/LanguageSt
 3. Select **Create new deployment** to create a new deployment and assign a trained model from the dropdown below. You can also **Overwrite an existing deployment** by selecting this option and select the trained model you want to assign to it from the dropdown below.
 
     > [!NOTE]
-    > Overwriting an existing deployment doesn't require changes to your [Prediction API](https://aka.ms/clu-apis) call but the results you get will be based on the newly assigned model.
+    > Overwriting an existing deployment doesn't require changes to your [Prediction API](https://aka.ms/clu-runtime-api) call but the results you get will be based on the newly assigned model.
     
     :::image type="content" source="../../media/create-deployment-job.png" alt-text="A screenshot showing the screen for adding a new deployment in Language Studio." lightbox="../../media/create-deployment-job.png":::
 

articles/cognitive-services/language-service/conversational-language-understanding/includes/rest-api/swap-deployment.md

@@ -15,7 +15,7 @@ Create a **POST** request using the following URL, headers, and JSON body to sta
 ### Request URL
 
 ```rest
-{ENDPOINT}/language/authoring/analyze-conversations/projects/{PROJECT-NAME}/deployments:swap?api-version={API-VERSION}
+{ENDPOINT}/language/authoring/analyze-conversations/projects/{PROJECT-NAME}/deployments/:swap?api-version={API-VERSION}
 ```
 
 |Placeholder  |Value  | Example |

articles/cognitive-services/language-service/conversational-language-understanding/overview.md

@@ -23,7 +23,7 @@ This documentation contains the following article types:
 
 * [Quickstarts](quickstart.md) are getting-started instructions to guide you through making requests to the service.
 * [Concepts](concepts/evaluation-metrics.md) provide explanations of the service functionality and features.
-* [How-to guides](how-to/tag-utterances.md) contain instructions for using the service in more specific or customized ways.
+* [How-to guides](how-to/create-project.md) contain instructions for using the service in more specific or customized ways.
 
 
 ## Example usage scenarios
@@ -61,9 +61,9 @@ Follow these steps to get the most out of your model:
 
 3. **Train model**: Your model starts learning from your labeled data.
 
-4. **View model evaluation details**: View the evaluation details for your model to determine how well it performs when introduced to new data.
+4. **Viewmodel evaluation details**: View the evaluation details for your model to determine how well it performs when introduced to new data.
 
-5. **Deploy model**: Deploying a model makes it available for use via the [Runtime API](https://aka.ms/clu-apis).
+5. **Deploy model**: Deploying a model makes it available for use via the [Runtime API](https://aka.ms/clu-runtime-api).
 
 6. **Predict intents and entities**: Use your custom model to predict intents and entities from user's utterances.
 
@@ -75,12 +75,12 @@ As you use CLU, see the following reference documentation and samples for Azure
 |---------|---------|---------|
 |REST APIs (Authoring)   | [REST API documentation](https://aka.ms/clu-authoring-apis)        |         |
 |REST APIs (Runtime)    | [REST API documentation](https://aka.ms/clu-apis)        |         |
-|C#     | [C# documentation](/dotnet/api/azure.ai.textanalytics?view=azure-dotnet-preview&preserve-view=true)        | [C# samples](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/cognitivelanguage/Azure.AI.Language.Conversations/samples)        |
-|Python | [Python documentation](/python/api/overview/azure/ai-textanalytics-readme?view=azure-python-preview&preserve-view=true)        | [Python samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples) |
+|C# (Runtime)    | [C# documentation](/dotnet/api/azure.ai.textanalytics?view=azure-dotnet-preview&preserve-view=true)        | [C# samples](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/cognitivelanguage/Azure.AI.Language.Conversations/samples)        |
+|Python (Runtime)| [Python documentation](/python/api/overview/azure/ai-textanalytics-readme?view=azure-python-preview&preserve-view=true)        | [Python samples](https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/cognitivelanguage/azure-ai-language-conversations/samples) |
 
 ## Responsible AI 
 
-An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it's deployed. Read the [transparency note for CLU]() to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
+An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it's deployed. Read the transparency note for CLU to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
 
 [!INCLUDE [Responsible AI links](../includes/overview-responsible-ai-links.md)]
 

articles/cognitive-services/language-service/conversational-language-understanding/service-limits.md

@@ -83,11 +83,11 @@ The following limits are observed for the conversational language understanding.
 
 | Item | Limits |
 |--|--|
-| Project name |  You can only use letters `(a-z, A-Z)`, and numbers `(0-9)` with no spaces. Maximum allowed length is 50 characters. |
+| Project name |  You can only use letters `(a-z, A-Z)`, and numbers `(0-9)` ,symbols  `_ . -`,with no spaces. Maximum allowed length is 50 characters. |
 | Model name |  You can only use letters `(a-z, A-Z)`, numbers `(0-9)` and symbols `_ . -`. Maximum allowed length is 50 characters.  |
 | Deployment name |  You can only use letters `(a-z, A-Z)`, numbers `(0-9)` and symbols `_ . -`. Maximum allowed length is 50 characters.  |
-| Intent name | You can only use letters `(a-z, A-Z)`, numbers `(0-9)` and symbols `_ . -`. Maximum allowed length is 50 characters.  |
-| Entity name | You can only use letters `(a-z, A-Z)`, numbers `(0-9)` and symbols `_ . -`. Maximum allowed length is 50 characters.  |
+| Intent name| You can only use letters `(a-z, A-Z)`, numbers `(0-9)` and all symbols except ":", `$ & %  * (  ) + ~ # / ?`. Maximum allowed length is 50 characters.|
+| Entity name| You can only use letters `(a-z, A-Z)`, numbers `(0-9)` and all symbols except ":", `$ & %  * (  ) + ~ # / ?`. Maximum allowed length is 50 characters.|
 
 ## Next steps
 

articles/cognitive-services/language-service/custom-named-entity-recognition/faq.md

@@ -44,7 +44,7 @@ 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 evaluation](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 tags 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 your data](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?