Merge pull request #275303 from eric-urban/eur/build-ai-studio-33

American-Dipper · web-flow · commit 8839206afce0 · 2024-05-13T15:54:06.000-07:00
[SCOPED] preview note
diff --git a/articles/ai-studio/ai-services/connect-ai-services.md b/articles/ai-studio/ai-services/connect-ai-services.md
@@ -15,6 +15,8 @@ author: eric-urban
 
 # Connect AI services to your hub in Azure AI Studio
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 You can try out AI services for free in Azure AI Studio as described in the [getting started with AI services](get-started.md) article. This article describes how to use AI services connections to do more via Azure AI Studio, SDKs, and APIs. 
 
 After you create a hub with AI services, you can use the AI services connection via the AI Studio UI, APIs, and SDKs. For example, you can try out AI services via **Home** > **AI Services** in the AI Studio UI as shown here.
diff --git a/articles/ai-studio/ai-services/get-started.md b/articles/ai-studio/ai-services/get-started.md
@@ -13,6 +13,8 @@ author: eric-urban
 
 # Get started with AI services in Azure AI Studio
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 This article describes how to get started with AI services in [Azure AI Studio](https://ai.azure.com). You can connect to AI services in Azure AI Studio to use AI capabilities such as Azure OpenAI, Speech, Language, Translator, Vision, Document Intelligence, and Content Safety.
 
 Go to the **Home** page and then select **AI Services** from the left pane to explore these services.
diff --git a/articles/ai-studio/how-to/deploy-jais-models.md b/articles/ai-studio/how-to/deploy-jais-models.md
@@ -14,7 +14,7 @@ ms.custom: [references_regions]
 
 # How to deploy JAIS with Azure AI Studio
 
-[!INCLUDE [Azure AI Studio preview](../includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
 
 In this article, you learn how to use Azure AI Studio to deploy the JAIS model as serverless APIs with pay-as-you-go token-based billing.
 
diff --git a/articles/ai-studio/how-to/deploy-models-phi-3.md b/articles/ai-studio/how-to/deploy-models-phi-3.md
@@ -15,7 +15,7 @@ ms.custom: [references_regions]
 
 # How to deploy Phi-3 family of small language models with Azure AI Studio
 
-[!INCLUDE [feature-preview](../includes/feature-preview.md)]
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
 
 In this article, you learn about the Phi-3 family of small language models (SLMs). You also learn two ways to use Azure AI Studio to deploy models from this family: deploy as serverless APIs with pay-as-you-go token-based billing or deploy with hosted infrastructure in real-time endpoints.
 
diff --git a/articles/ai-studio/how-to/deploy-models-serverless-availability.md b/articles/ai-studio/how-to/deploy-models-serverless-availability.md
@@ -18,6 +18,8 @@ ms.custom:
 
 # Region availability for models in Serverless API endpoints
 
+[!INCLUDE [Feature preview](../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.
diff --git a/articles/ai-studio/how-to/disaster-recovery.md b/articles/ai-studio/how-to/disaster-recovery.md
@@ -13,6 +13,8 @@ ms.date: 5/21/2024
 
 # Customer enabled disaster recovery
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 To maximize your uptime, plan ahead to maintain business continuity and prepare for disaster recovery with Azure AI Studio. Since Azure AI Studio builds on [Azure Machine Learning architecture](/azure/machine-learning/concept-workspace), it's beneficial to reference the foundational architecture.
 
 Microsoft strives to ensure that Azure services are always available. However, unplanned service outages might occur. We recommend having a disaster recovery plan in place for handling regional service outages. In this article, you learn how to:
diff --git a/articles/ai-studio/how-to/model-catalog-overview.md b/articles/ai-studio/how-to/model-catalog-overview.md
@@ -15,6 +15,8 @@ author: ssalgadodev
 
 # Model Catalog and Collections in Azure AI Studio
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 The model catalog in Azure AI studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models across model providers such as Azure OpenAI service, Mistral, Meta, Cohere, Nvidia, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.   
 
 ## Model Collections 
diff --git a/articles/ai-studio/reference/reference-model-inference-api.md b/articles/ai-studio/reference/reference-model-inference-api.md
@@ -16,6 +16,8 @@ ms.custom:
 
 # Azure AI Model Inference API
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 The Azure AI Model Inference is an API that exposes a common set of capabilities for foundational models and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way. Developers can talk with different models deployed in Azure AI Studio without changing the underlying code they are using.
 
 ## Benefits
diff --git a/articles/ai-studio/reference/reference-model-inference-chat-completions.md b/articles/ai-studio/reference/reference-model-inference-chat-completions.md
@@ -16,6 +16,8 @@ ms.custom:
 
 # Reference: Chat Completions
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 Creates a model response for the given chat conversation.
 
 ```http
diff --git a/articles/ai-studio/reference/reference-model-inference-completions.md b/articles/ai-studio/reference/reference-model-inference-completions.md
@@ -16,6 +16,8 @@ ms.custom:
 
 # Reference: Completions
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 Creates a completion for the provided prompt and parameters.
 
 ```http
diff --git a/articles/ai-studio/reference/reference-model-inference-embeddings.md b/articles/ai-studio/reference/reference-model-inference-embeddings.md
@@ -16,6 +16,8 @@ ms.custom:
 
 # Reference: Embeddings
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 Creates an embedding vector representing the input text.
 
 ```http
diff --git a/articles/ai-studio/reference/reference-model-inference-images-embeddings.md b/articles/ai-studio/reference/reference-model-inference-images-embeddings.md
@@ -16,6 +16,8 @@ ms.custom:
 
 # Reference: Image Embeddings
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 Creates an embedding vector representing the input image and text pair.
 
 ```http
diff --git a/articles/ai-studio/reference/reference-model-inference-info.md b/articles/ai-studio/reference/reference-model-inference-info.md
@@ -16,6 +16,8 @@ ms.custom:
 
 # Reference: Info
 
+[!INCLUDE [Feature preview](../includes/feature-preview.md)]
+
 Returns the information about the model deployed under the endpoint.
 
 ```http
diff --git a/articles/ai-studio/what-is-ai-studio.md b/articles/ai-studio/what-is-ai-studio.md
@@ -15,8 +15,6 @@ ms.custom: ignite-2023
 
 # What is Azure AI Studio?
 
-[!INCLUDE [Feature preview](./includes/feature-preview.md)]
-
 [Azure AI Studio](https://ai.azure.com) is a trusted platform that empowers developers to drive innovation and shape the future with AI in a safe, secure, and responsible way. The comprehensive platform accelerates the development of production-ready copilots to support enterprise chat, content generation, data analysis, and more. Developers can explore cutting-edge APIs and models for their use cases; build and test solutions with collaborative and responsible AI tools, safeguards, and best practices; deploy AI innovations for use in websites, applications, and other production environments; and manage solutions with continuous monitoring and governance in production.
 
 :::image type="content" source="./media/explore/ai-studio-home.png" alt-text="Screenshot of the Azure AI Studio home page." lightbox="./media/explore/ai-studio-home.png":::
