Merge pull request #5179 from aahill/language-freshness

freshness updates

Author: Stacyrch140

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

@@ -0,0 +1,49 @@
+---
+author: jboback
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 11/21/2024
+ms.author: jboback
+---
+
+## Prerequisites
+
+* Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
+
+## Sign in to Language Studio
+
+[!INCLUDE [Sign in to Language studio](../language-studio/sign-in-studio.md)]
+
+
+## Create a conversational language understanding project
+
+Once you have a Language resource selected, create a conversational language understanding project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.
+
+For this quickstart, you can download [this sample project file](https://go.microsoft.com/fwlink/?linkid=2196152) and import it. This project can predict the intended commands from user input, such as: reading emails, deleting emails, and attaching a document to an email. 
+
+[!INCLUDE [Import project](../language-studio/import-project.md)]
+
+Once the upload is complete, you will land on **Schema definition** page. For this quickstart, the schema is already built, and utterances are already labeled with intents and entities.
+
+## Train your model
+
+Typically, after you create a project, you should [build a schema](../../how-to/build-schema.md) and [label utterances](../../how-to/tag-utterances.md). For this quickstart, we already imported a ready project with built schema and labeled utterances. 
+ 
+To train a model, you need to start a training job. The output of a successful training job is your trained model.
+
+[!INCLUDE [Train model](../language-studio/train-model.md)]
+
+## Deploy your model
+
+Generally after training a model you would review its evaluation details. In this quickstart, you will just deploy your model, and make it available for you to try in Language studio, or you can call the [prediction API](https://aka.ms/clu-apis).
+
+[!INCLUDE [Deploy model](../language-studio/deploy-model.md)]
+
+## Test deployed model
+
+[!INCLUDE [Test model](../language-studio/test-model.md)]
+
+## Clean up resources
+
+[!INCLUDE [Delete project using Language studio](../language-studio/delete-project.md)]

articles/ai-services/language-service/conversational-language-understanding/quickstart.md

@@ -6,17 +6,28 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 05/23/2025
+ms.date: 05/01/2025
 ms.author: jboback
 ms.custom: language-service-clu, mode-other
+zone_pivot_groups: usage-custom-language-features
 ---
 
 # Quickstart: Conversational language understanding 
 
 Use this article to get started with Conversational Language understanding using Azure AI Foundry and the REST API. Follow these steps to try out an example.
 
+::: zone pivot="language-studio"
+
+[!INCLUDE [Language Studio quickstart](includes/quickstarts/language-studio.md)]
+
+::: zone-end
+
+::: zone pivot="rest-api"
+
 [!INCLUDE [REST API quickstart](includes/quickstarts/rest-api.md)]
 
+::: zone-end
+
 ## Next steps
 
 * [Learn about entity components](concepts/entity-components.md)

articles/ai-services/language-service/named-entity-recognition/quickstart.md

@@ -6,7 +6,7 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 04/29/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ms.devlang: csharp
 # ms.devlang: csharp, java, javascript, python

articles/ai-services/language-service/sentiment-opinion-mining/how-to/call-api.md

@@ -6,14 +6,14 @@ author: jboback
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 04/29/2025
+ms.date: 05/23/2025
 ms.author: jboback
 ms.custom: language-service-sentiment-opinion-mining
 ---
 
 # How to: Use Sentiment analysis and Opinion Mining 
 
-Sentiment analysis and opinion mining are two ways of detecting positive and negative sentiment. Using sentiment analysis, you can get sentiment labels (such as "negative", "neutral" and "positive") and confidence scores at the sentence and document-level. Opinion Mining provides granular information about the opinions related to words (such as the attributes of products or services) in the text.
+Sentiment analysis and opinion mining are two ways of detecting positive and negative sentiment. Using sentiment analysis, you can get sentiment labels (such as "negative" "neutral" and "positive") and confidence scores at the sentence and document-level. Opinion Mining provides granular information about the opinions related to words (such as the attributes of products or services) in the text.
 
 ## Sentiment Analysis