Merge pull request #268324 from MicrosoftDocs/main

3/6/2024 PM Publish

Author: Taojunshen

.whatsnew/.azure-monitor.json

@@ -129,7 +129,7 @@ Mapping these fields correctly helps ensure the model has better response and ci
 
 ### Search filter (API)
 
-If you want to implement additional value-based criteria for query execution, you can set up a search filter using the `filter` parameter in the [REST API](../references/azure-search.md).
+If you want to implement additional value-based criteria for query execution, you can set up a [search filter](/azure/search/search-filters) using the `filter` parameter in the [REST API](../references/azure-search.md).
 
 
 # [Azure Cosmos DB for MongoDB vCore](#tab/mongo-db)

.whatsnew/.defender-for-iot.json

@@ -102,7 +102,7 @@ node.exe ChatWithOwnData.js
 ## Output
 
 ```output
-Message: What are the differences between Azure Machine Learning and Azure AI services?
+Message: Tell me something interesting
 Based on the retrieved document, an interesting fact is...
 
 ```

.whatsnew/.security-center.json

@@ -20,7 +20,7 @@ ms.custom:
 Azure AI services help developers and organizations rapidly create intelligent, cutting-edge, market-ready, and responsible applications with out-of-the-box and pre-built and customizable APIs and models. Example applications include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making. 
 
 > [!TIP]
-> Try Azure AI services including Azure OpenAI, Content Safety, Speech, Vision, amd more in [Azure AI Studio](https://ai.azure.com). For more information, see [What is Azure AI Studio?](../ai-studio/what-is-ai-studio.md).
+> Try Azure AI services including Azure OpenAI, Content Safety, Speech, Vision, and more in [Azure AI Studio](https://ai.azure.com). For more information, see [What is Azure AI Studio?](../ai-studio/what-is-ai-studio.md).
 
 Most Azure AI services are available through REST APIs and client library SDKs in popular development languages. For more information, see each service's documentation.
 

.whatsnew/.sentinel.json

@@ -22,7 +22,7 @@ Azure AI Studio includes a content filtering system that works alongside core mo
 > [!IMPORTANT]
 > The content filtering system isn't applied to prompts and completions processed by the Whisper model in Azure OpenAI Service. Learn more about the [Whisper model in Azure OpenAI](../../ai-services/openai/concepts/models.md#whisper-preview).
 
-This system is powered by Azure AI Content Safety, and now works by running both the prompt and completion through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Variations in API configurations and application design might affect completions and thus filtering behavior.
+This system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and works by running both the prompt and completion through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
 The content filtering models have been trained and tested on the following languages: English, German, Japanese, Spanish, French, Italian, Portuguese, and Chinese. However, the service can work in many other languages, but the quality can vary. In all cases, you should do your own testing to ensure that it works for your application.
 
@@ -86,5 +86,5 @@ Now, you can go to the playground to test whether the content filter works as ex
 ## Next steps
 
 - Learn more about the [underlying models that power Azure OpenAI](../../ai-services/openai/concepts/models.md).
-- Azure AI Studio content filtering is powered by [Azure AI Content Safety](/azure/ai-services/content-safety/overview).
+- Azure AI Studio content filtering is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md).
 - Learn more about understanding and mitigating risks associated with your application: [Overview of Responsible AI practices for Azure OpenAI models](/legal/cognitive-services/openai/overview?context=/azure/ai-services/context/context).

articles/ai-services/openai/concepts/use-your-data.md

@@ -5,23 +5,21 @@ description: Learn how to deploy Llama 2 family of large language models with Az
 manager: scottpolly
 ms.service: azure-ai-studio
 ms.topic: how-to
-ms.date: 02/09/2024
+ms.date: 3/6/2024
 ms.reviewer: fasantia
 ms.author: mopeakande
 author: msakande
 ms.custom: [references_regions]
-
-#This functionality is also available in Azure Machine Learning: /azure/machine-learning/how-to-deploy-models-llama.md
 ---
 
 # How to deploy Llama 2 family of large language models with Azure AI Studio
 
+[!INCLUDE [Azure AI Studio preview](../includes/preview-ai-studio.md)]
+
 In this article, you learn about the Llama 2 family of large language models (LLMs). You also learn how to use Azure AI Studio to deploy models from this set either as a service with pay-as you go billing or with hosted infrastructure in real-time endpoints.
 
 The Llama 2 family of LLMs is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. The model family also includes fine-tuned versions optimized for dialogue use cases with reinforcement learning from human feedback (RLHF), called Llama-2-chat.
 
-[!INCLUDE [Azure AI Studio preview](../includes/preview-ai-studio.md)]
-
 ## Deploy Llama 2 models with pay-as-you-go
 
 Certain models in the model catalog can be deployed as a service with pay-as-you-go, providing a way to consume them as an API without hosting them on your subscription, while keeping the enterprise security and compliance organizations need. This deployment option doesn't require quota from your subscription.
@@ -47,7 +45,7 @@ If you need to deploy a different model, [deploy it to real-time endpoints](#dep
 - An [Azure AI hub resource](../how-to/create-azure-ai-resource.md).
 
     > [!IMPORTANT]
-    > Pay-as-you-go model deployment offering is only available in AI hubs created in **East US 2** and **West US 3** regions.
+    > For Llama 2 family models, the pay-as-you-go model deployment offering is only available with AI hubs created in **East US 2** and **West US 3** regions.
 
 - An [Azure AI project](../how-to/create-projects.md) in Azure AI Studio.
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __owner__ or __contributor__ role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:
@@ -77,9 +75,9 @@ To create a deployment:
 1. Sign in to [Azure AI Studio](https://ai.azure.com).
 1. Choose the model you want to deploy from the Azure AI Studio [model catalog](https://ai.azure.com/explore/models). 
 
-    Alternatively, you can initiate deployment by starting from your project in AI Studio. From the **Build** tab of your project, select the **Deployments** option, then select **+ Create**.
+    Alternatively, you can initiate deployment by starting from your project in AI Studio. From the **Build** tab of your project, select **Deployments** > **+ Create**.
 
-1. On the model's **Details** page, select **Deploy** and then **Pay-as-you-go**.
+1. On the model's **Details** page, select **Deploy** and then select **Pay-as-you-go**.
 
     :::image type="content" source="../media/deploy-monitor/llama/deploy-pay-as-you-go.png" alt-text="A screenshot showing how to deploy a model with the pay-as-you-go option." lightbox="../media/deploy-monitor/llama/deploy-pay-as-you-go.png":::