Merge pull request #281009 from ssalgadodev/patch-126

Update deploy-models-serverless.md

Author: prmerger-automator[bot]

articles/ai-studio/concepts/deployments-overview.md

@@ -16,7 +16,6 @@ author: msakande
 
 # Overview: Deploy models, flows, and web apps with Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
 
 Azure AI Studio supports deploying large language models (LLMs), flows, and web apps. Deploying an LLM or flow makes it available for use in a website, an application, or other production environments. This typically involves hosting the model on a server or in the cloud, and creating an API or other interface for users to interact with the model. 
 

articles/ai-studio/how-to/deploy-models-serverless-availability.md

@@ -18,8 +18,6 @@ ms.custom:
 
 # Region availability for models in serverless API endpoints | Azure AI Studio
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
-
 In this article, you learn about which regions are available for each of the models supporting serverless API endpoint deployments.
 
 Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.

articles/ai-studio/how-to/deploy-models-serverless-connect.md

@@ -17,8 +17,6 @@ ms.custom:
 
 # Consume serverless API endpoints from a different Azure AI Studio project or hub
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
-
 In this article, you learn how to configure an existing serverless API endpoint in a different project or hub than the one that was used to create the deployment.
 
 [Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as serverless APIs. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.
@@ -235,4 +233,4 @@ Follow these steps to create a connection:
 ## Related content
 
 - [What is Azure AI Studio?](../what-is-ai-studio.md)
-- [Azure AI FAQ article](../faq.yml)
+- [Azure AI FAQ article](../faq.yml)

articles/ai-studio/how-to/deploy-models-serverless.md

@@ -15,8 +15,6 @@ ms.custom: build-2024, serverless, devx-track-azurecli
 
 # Deploy models as serverless APIs
 
-[!INCLUDE [Feature preview](~/reusable-content/ce-skilling/azure/includes/ai-studio/includes/feature-preview.md)]
-
 In this article, you learn how to deploy a model from the model catalog as a serverless API with pay-as-you-go token based billing.
 
 [Certain models in the model catalog](deploy-models-serverless-availability.md) can be deployed as a serverless API with pay-as-you-go billing. This kind of deployment provides a way to consume models as an API without hosting them on your subscription, while keeping the enterprise security and compliance that organizations need. This deployment option doesn't require quota from your subscription.