content safety for models curated by AzureAI

Author: msakande

articles/ai-studio/concepts/content-filtering.md

@@ -26,7 +26,7 @@ Azure AI Foundry includes a content filtering system that works alongside core m
 
 This content filtering system is powered by [Azure AI Content Safety](../../ai-services/content-safety/overview.md), and it works by running both the prompt input and completion output through an ensemble of classification models aimed at detecting and preventing the output of harmful content. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
-With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on).  Models available through **serverless APIs** have content filtering enabled by default. To learn more about the default content filter enabled for serverless APIs, see [Content safety for models in the model catalog](model-catalog-content-safety.md).
+With Azure OpenAI model deployments, you can use the default content filter or create your own content filter (described later on).  Models available through **serverless APIs** have content filtering enabled by default. To learn more about the default content filter enabled for serverless APIs, see [Content safety for models curated by Azure AI in the model catalog](model-catalog-content-safety.md).
 
 ## Language support
 

articles/ai-studio/concepts/model-catalog-content-safety.md

@@ -1,5 +1,5 @@
 ---
-title: Content safety for models in the model catalog
+title: Content safety for models curated by Azure AI in the model catalog
 titleSuffix: Azure AI Foundry
 description: Learn about content safety for models deployed using serverless APIs, using Azure AI Foundry.
 manager: scottpolly
@@ -13,7 +13,7 @@ reviewer: ositanachi
 ms.custom: 
 ---
 
-# Content safety for models in the model catalog
+# Content safety for models curated by Azure AI in the model catalog
 
 [!INCLUDE [feature-preview](../includes/feature-preview.md)]
 

articles/ai-studio/how-to/model-catalog-overview.md

@@ -27,7 +27,7 @@ The model catalog in Azure AI Foundry portal is the hub to discover and use a wi
 The model catalog organizes models into different collections:
 
 
-* **Curated by Azure AI**: The most popular non-Microsoft open-weight and proprietary models packaged and optimized to work seamlessly on the Azure AI platform. Use of these models is subject to the model providers' license terms. When you deploy these models in Azure AI Foundry portal, their availability is subject to the applicable [Azure service-level agreement (SLA)](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services), and Microsoft provides support for deployment problems.
+* **Curated by Azure AI**: The most popular partner models (open-weight and proprietary) packaged and optimized to work seamlessly on the Azure AI platform. Use of these models is subject to the model providers' license terms. When you deploy these models in Azure AI Foundry portal, their availability is subject to the applicable [Azure service-level agreement (SLA)](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services), and Microsoft provides support for deployment problems.
 
   Models from partners such as Meta, NVIDIA, and Mistral AI are examples of models available in this collection on the catalog. You can identify these models by looking for a green checkmark on the model tiles in the catalog. Or you can filter by the **Curated by Azure AI** collection.
 

articles/ai-studio/includes/content-safety-serverless-apis-note.md

@@ -10,4 +10,4 @@ ms.custom: include file
 ---
 
 > [!NOTE]
-> Azure AI content safety is currently available for models deployed as serverless API endpoints, but not to models deployed via managed compute. To learn more about Azure AI content safety for models deployed as serverless APIs, see [Content safety for models in the model catalog](../concepts/model-catalog-content-safety.md).
+> Azure AI content safety is currently available for models deployed as serverless API endpoints, but not to models deployed via managed compute. To learn more about Azure AI content safety for models deployed as serverless APIs, see [Content safety for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md).

articles/ai-studio/includes/content-safety-serverless-models.md

@@ -13,7 +13,7 @@ ms.custom: include file
 # Also used in Azure Machine Learning documentation
 ---
 
-For language models deployed via serverless APIs, Azure AI implements a default configuration of [Azure AI Content Safety](../../ai-services/content-safety/overview.md) text moderation filters that detect harmful content such as hate, self-harm, sexual, and violent content. To learn more about content filtering (preview), see [Content safety for models in the model catalog](../concepts/model-catalog-content-safety.md). 
+For language models deployed via serverless APIs, Azure AI implements a default configuration of [Azure AI Content Safety](../../ai-services/content-safety/overview.md) text moderation filters that detect harmful content such as hate, self-harm, sexual, and violent content. To learn more about content filtering (preview), see [Content safety for models curated by Azure AI in the model catalog](../concepts/model-catalog-content-safety.md). 
 
 > [!TIP]
 > Content filtering (preview) is not available for certain model types that are deployed via serverless APIs. These model types include embedding models and time series models.

articles/ai-studio/toc.yml

@@ -198,7 +198,7 @@ items:
         - name: Consume serverless API models from a different project or hub
           href: how-to/deploy-models-serverless-connect.md
           displayName: maas, paygo, models-as-a-service
-        - name: Content safety for models deployed with serverless API
+        - name: Content safety for models deployed with serverless APIs
           href: concepts/model-catalog-content-safety.md
         - name: Model and region availability for Serverless API deployments
           href: how-to/deploy-models-serverless-availability.md