Azure Openai rebrand

Author: ivorb

learn-pr/wwl-data-ai/ai-agent-fundamentals/includes/3-agent-development.md

@@ -12,7 +12,7 @@ Azure AI Foundry Agent Service is a managed service in Azure that is designed to
 
 ## OpenAI Assistants API
 
-The OpenAI Assistants API provides a subset of the features in Foundry Agent Service, and can only be used with OpenAI models. In Azure, you can use the Assistants API with the Azure OpenAI service, though in practice the Foundry Agent Service provides greater flexibility and functionality for agent development on Azure.
+The OpenAI Assistants API provides a subset of the features in Foundry Agent Service, and can only be used with OpenAI models. In Azure, you can use the Assistants API with Azure OpenAI, though in practice the Foundry Agent Service provides greater flexibility and functionality for agent development on Azure.
 
 > [!TIP]
 > For more information about using the OpenAI Assistants API in Azure, see **[Getting started with Azure OpenAI Assistants](/azure/ai-services/openai/how-to/assistant)**.

learn-pr/wwl-data-ai/ai-foundry-sdk/includes/04-chat-client.md

@@ -3,7 +3,7 @@ A common scenario in an AI application is to connect to a generative AI model an
 The specific libraries and code used to build a chat client depends on how the target model has been deployed in the Azure AI Foundry project. You can deploy models to the following model hosting solutions:
 
 - **Azure AI model inference**: A single endpoint for multiple models of different types, including OpenAI models and others from the Azure AI Foundry model catalog. Models are consumed through an **Azure AI services** resource connection in the project.
-- **Azure OpenAI service**: A single endpoint for OpenAI models hosted in Azure. Models are consumed through an **Azure OpenAI service** resource connection in the project.
+- **Azure OpenAI in Foundry Models**: A single endpoint for OpenAI models hosted in Azure. Models are consumed through an **Azure OpenAI** resource connection in the project.
 - **Serverless API**: A model-as-a-service solution in which each deployed model is accessed through a unique endpoint and hosted in the Azure AI Foundry project.
 - **Managed compute**: A model-as-a-service solution in which each deployed model is accessed through a unique endpoint hosted in custom compute.
 
@@ -119,13 +119,13 @@ namespace my_foundry_client
 
 ::: zone-end
 
-## Building a client app for Azure OpenAI service deployments
+## Building a client app for Azure OpenAI deployments
 
-When you have deployed models to the Azure OpenAI service, you can use the **AIProjectConnection** to connect to the Azure OpenAI service resource in your project and then use the Azure OpenAI SDK to chat with your models.
+When you have deployed models to Azure OpenAI, you can use the **AIProjectConnection** to connect to the Azure OpenAI resource in your project and then use the Azure OpenAI SDK to chat with your models.
 
 ::: zone pivot="python"
 
-In the Azure AI Foundry SDK for Python, the **AIProjectClient** class provides a **get_azure_openai_client()** method that you can use to create an Azure OpenAI client object. You can then use the classes and methods defined in the Azure OpenAI SDK to consume a model deployed to the Azure OpenAI service.
+In the Azure AI Foundry SDK for Python, the **AIProjectClient** class provides a **get_azure_openai_client()** method that you can use to create an Azure OpenAI client object. You can then use the classes and methods defined in the Azure OpenAI SDK to consume a model deployed to Azure OpenAI.
 
 The following Python code sample uses the Azure AI Foundry and Azure OpenAI SDKs to chat with a model deployment named **gpt-4-model**.
 
@@ -170,7 +170,7 @@ except Exception as ex:
 
 ::: zone pivot="csharp"
 
-To chat with a model that is deployed to the Azure OpenAI service in an Azure AI Foundry project from C#, use the **AIProjectClient** object to retrieve the default Azure OpenAI resource connection from the project, and then use the Azure OpenAI SDK to connect to the service and send prompts to the model.
+To chat with a model that is deployed to Azure OpenAI in an Azure AI Foundry project from C#, use the **AIProjectClient** object to retrieve the default Azure OpenAI resource connection from the project, and then use the Azure OpenAI SDK to connect to the service and send prompts to the model.
 
 The following C# code sample uses the Azure AI Foundry and Azure OpenAI SDKs to chat with a model deployment named **gpt-4-model**.
 

learn-pr/wwl-data-ai/build-copilot-ai-studio/includes/3-search-data.md

@@ -30,7 +30,7 @@ When you want to be able to use vector search to search your data, you need to c
 ![Diagram showing a vector index that contains embeddings.](../media/vector-index.png)
 
 > [!Tip]
-> Learn more about [embeddings in the Azure OpenAI Service](/azure/ai-services/openai/concepts/understand-embeddings?azure-portal=true).
+> Learn more about [embeddings in the Azure OpenAI in Foundry Models](/azure/ai-services/openai/concepts/understand-embeddings?azure-portal=true).
 
 ## Creating a search index
 

learn-pr/wwl-data-ai/build-language-solution-azure-openai/6-knowledge-check.yml

@@ -1,50 +1,50 @@
-### YamlMime:ModuleUnit
-uid: learn.wwl.build-language-solution-azure-openai.knowledge-check
-title: Module assessment
-metadata:
-  title: Module assessment
-  description: Check your understanding of the content presented in this module.
-  author: ivorb
-  ms.author: berryivor
-  ms.date: 09/04/2024
-  ms.topic: unit
-  ms.collection:
-    - wwl-ai-copilot
-durationInMinutes: 2
-content: |
-quiz:  
-  questions:  
-  - content: "What resource values are required to make requests to your Azure OpenAI resource?"  
-    choices:  
-    - content: "Chat, Embedding, and Completion"  
-      isCorrect: false  
-      explanation: "Each call to the Azure OpenAI service requires a key, endpoint, and deployment name."  
-    - content: "Key, Endpoint, and Deployment name"  
-      isCorrect: true  
-      explanation: "Each call to the Azure OpenAI service requires a key, endpoint, and deployment name."  
-    - content: "Summary, Deployment name, and Endpoint"  
-      isCorrect: false  
-      explanation: "Each call to the Azure OpenAI service requires a key, endpoint, and deployment name."  
-  - content: "What are the three available endpoints for interacting with a deployed Azure OpenAI model?"  
-    choices:  
-    - content: "Completion, ChatCompletion, and Translation"  
-      isCorrect: false  
-      explanation: "Translation isn't an Azure OpenAI endpoint."  
-    - content: "Completion, ChatCompletion, and Embeddings"  
-      isCorrect: true  
-      explanation: "Completion, ChatCompletion, and Embeddings are the three available endpoints"  
-    - content: "Deployment, Summary, and Similarity"  
-      isCorrect: false  
-      explanation: "Completion, ChatCompletion, and Embeddings are the three available endpoints"  
-  - content: "What is the best available endpoint to model the next completion of a conversation in Azure OpenAI?"  
-    choices:  
-    - content: "ChatCompletion"  
-      isCorrect: true  
-      explanation: "The best model to use for the next response in a conversation is ChatCompletion"  
-    - content: "Embeddings"  
-      isCorrect: false  
-      explanation: "The best model to use for the next response in a conversation is ChatCompletion"  
-    - content: "TranslateCompletion"  
-      isCorrect: false  
-      explanation: "The best model to use for the next response in a conversation is ChatCompletion"  
-
+### YamlMime:ModuleUnit
+uid: learn.wwl.build-language-solution-azure-openai.knowledge-check
+title: Module assessment
+metadata:
+  title: Module assessment
+  description: Check your understanding of the content presented in this module.
+  author: ivorb
+  ms.author: berryivor
+  ms.date: 09/04/2024
+  ms.topic: unit
+  ms.collection:
+    - wwl-ai-copilot
+durationInMinutes: 2
+content: |
+quiz:  
+  questions:  
+  - content: "What resource values are required to make requests to your Azure OpenAI resource?"  
+    choices:  
+    - content: "Chat, Embedding, and Completion"  
+      isCorrect: false  
+      explanation: "Each call to Azure OpenAI requires a key, endpoint, and deployment name."  
+    - content: "Key, Endpoint, and Deployment name"  
+      isCorrect: true  
+      explanation: "Each call to Azure OpenAI requires a key, endpoint, and deployment name."  
+    - content: "Summary, Deployment name, and Endpoint"  
+      isCorrect: false  
+      explanation: "Each call to Azure OpenAI requires a key, endpoint, and deployment name."  
+  - content: "What are the three available endpoints for interacting with a deployed Azure OpenAI model?"  
+    choices:  
+    - content: "Completion, ChatCompletion, and Translation"  
+      isCorrect: false  
+      explanation: "Translation isn't an Azure OpenAI endpoint."  
+    - content: "Completion, ChatCompletion, and Embeddings"  
+      isCorrect: true  
+      explanation: "Completion, ChatCompletion, and Embeddings are the three available endpoints"  
+    - content: "Deployment, Summary, and Similarity"  
+      isCorrect: false  
+      explanation: "Completion, ChatCompletion, and Embeddings are the three available endpoints"  
+  - content: "What is the best available endpoint to model the next completion of a conversation in Azure OpenAI?"  
+    choices:  
+    - content: "ChatCompletion"  
+      isCorrect: true  
+      explanation: "The best model to use for the next response in a conversation is ChatCompletion"  
+    - content: "Embeddings"  
+      isCorrect: false  
+      explanation: "The best model to use for the next response in a conversation is ChatCompletion"  
+    - content: "TranslateCompletion"  
+      isCorrect: false  
+      explanation: "The best model to use for the next response in a conversation is ChatCompletion"  
+

learn-pr/wwl-data-ai/develop-applications-openai/1-introduction.yml

@@ -3,7 +3,7 @@ uid: learn.wwl.develop-applications-openai.introduction
 title: Introduction
 metadata:
   title: Introduction
-  description: Learn how to start using Azure OpenAI Service. This unit introduces possible use cases for Azure OpenAI Service.
+  description: Learn how to start using Azure OpenAI in Foundry Models. This unit introduces possible use cases for Azure OpenAI.
   author: ivorb
   ms.author: berryivor
   ms.date: 03/18/2025

learn-pr/wwl-data-ai/develop-applications-openai/2-getting-started.yml

@@ -1,9 +1,9 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.develop-applications-openai.getting-started
-title: Access Azure OpenAI Service
+title: Access Azure OpenAI in Foundry Models
 metadata:
-  title: Access Azure OpenAI Service
-  description: This unit explains how to apply for access to Azure OpenAI Service and how to create an Azure OpenAI resource. This unit explains the first steps to working with Azure OpenAI Service.
+  title: Access Azure OpenAI in Foundry Models
+  description: This unit explains how to apply for access to Azure OpenAI and how to create an Azure OpenAI resource. This unit explains the first steps to working with Azure OpenAI.
   author: ivorb
   ms.author: berryivor
   ms.date: 03/18/2025

learn-pr/wwl-data-ai/develop-applications-openai/7-exercise.yml

@@ -1,9 +1,9 @@
 ### YamlMime:ModuleUnit
 uid: learn.wwl.develop-applications-openai.exercise
-title: Exercise -  Get started with Azure OpenAI Service
+title: Exercise -  Get started with Azure OpenAI
 metadata:
-  title: Exercise -  Get started with Azure OpenAI Service
-  description: This unit contains a hands-on exercise. Learners can use the exercise to try out Azure OpenAI Service's prompt engineering capabilities.
+  title: Exercise -  Get started with Azure OpenAI
+  description: This unit contains a hands-on exercise. Learners can use the exercise to try out Azure OpenAI's prompt engineering capabilities.
   author: ivorb
   ms.author: berryivor
   ms.date: 03/18/2025

learn-pr/wwl-data-ai/develop-applications-openai/includes/1-introduction.md

@@ -2,4 +2,4 @@ Suppose you want to build a support application that summarizes text and suggest
 
 Generative AI models power ChatGPT's ability to produce new content, such as text, code, and images, based on natural language prompts. Many generative AI models are a subset of [deep learning algorithms](/dotnet/machine-learning/deep-learning-overview?azure-portal=true). These algorithms support various workloads across vision, speech, language, decision, search, and more.
 
-Azure OpenAI Service brings these generative AI models to the Azure platform, enabling you to develop powerful AI solutions that benefit from the security, scalability, and integration of other services provided by the Azure cloud platform. These models are available for building applications through a REST API, various SDKs, and a Studio interface. This module guides you through the Azure AI Foundry experience, giving you the foundation to further develop solutions with generative AI.
+Azure OpenAI in Foundry Models brings these generative AI models to the Azure platform, enabling you to develop powerful AI solutions that benefit from the security, scalability, and integration of other services provided by the Azure cloud platform. These models are available for building applications through a REST API, various SDKs, and a Studio interface. This module guides you through the Azure AI Foundry experience, giving you the foundation to further develop solutions with generative AI.

learn-pr/wwl-data-ai/develop-applications-openai/includes/2-getting-started.md

@@ -1,14 +1,14 @@
 The first step in building a generative AI solution with Azure OpenAI is to provision an Azure OpenAI resource in your Azure subscription. You can get started by creating a resource in the [Azure portal](https://portal.azure.com/?azure-portal=true) or with the Azure command line interface (CLI).
 
-## Create an Azure OpenAI Service resource in the Azure portal
+## Create an Azure OpenAI resource in the Azure portal
 
-When you create an Azure OpenAI Service resource, you need to provide a subscription name, resource group name, region, unique instance name, and select a pricing tier.
+When you create an Azure OpenAI resource, you need to provide a subscription name, resource group name, region, unique instance name, and select a pricing tier.
 
-![Screenshot of the Azure portal's page to create an Azure OpenAI Service resource.](../media/create-azure-openai-portal.png)
+![Screenshot of the Azure portal's page to create an Azure OpenAI resource.](../media/create-azure-openai-portal.png)
 
-## Create an Azure OpenAI Service resource in Azure CLI
+## Create an Azure OpenAI resource in Azure CLI
 
-To create an Azure OpenAI Service resource from the CLI, refer to this example and replace the following variables with your own:
+To create an Azure OpenAI resource from the CLI, refer to this example and replace the following variables with your own:
 
 - MyOpenAIResource: *replace with a unique name for your resource*
 - OAIResourceGroup: *replace with your resource group name* 
@@ -26,15 +26,15 @@ az cognitiveservices account create \
 ```
 
 >[!NOTE]
->You can find the regions available for a service through the CLI command `az account list-locations`. To see how to sign into Azure and create an Azure group via the CLI, you can refer to the [documentation here](/azure/cognitive-services/openai/how-to/create-resource?pivots=cli#sign-in-to-the-cli?azure-portal=true). Azure OpenAI Service provides access to many types of models. Certain models are only available in select regions. Consult the [Azure OpenAI model availability guide](/azure/cognitive-services/openai/concepts/models#model-summary-table-and-region-availability/?azure-portal=true) for region availability. You can create two Azure OpenAI resources per region.
+>You can find the regions available for a service through the CLI command `az account list-locations`. To see how to sign into Azure and create an Azure group via the CLI, you can refer to the [documentation here](/azure/cognitive-services/openai/how-to/create-resource?pivots=cli#sign-in-to-the-cli?azure-portal=true). Azure OpenAI provides access to many types of models. Certain models are only available in select regions. Consult the [Azure OpenAI model availability guide](/azure/cognitive-services/openai/concepts/models#model-summary-table-and-region-availability/?azure-portal=true) for region availability. You can create two Azure OpenAI resources per region.
 
 ## Use Azure OpenAI Foundry
 
 Azure AI Foundry provides access to model management, deployment, experimentation, customization, and learning resources.
 
 You can access the Azure AI Foundry through the Azure portal after creating a resource, or at [https://ai.azure.com/](https://ai.azure.com/) by signing in to your Azure account. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
 
-When you first open Azure AI Foundry, you'll want to navigate to the **Azure OpenAI** page (where you focus on only Azure OpenAI Service models), select your resource if you haven't already, and deploy your first model. To do so, select the **Deployments** page, from where you can deploy a base model and start experimenting with it.
+When you first open Azure AI Foundry, you'll want to navigate to the **Azure OpenAI** page (where you focus on only Azure OpenAI models), select your resource if you haven't already, and deploy your first model. To do so, select the **Deployments** page, from where you can deploy a base model and start experimenting with it.
 
 >[!NOTE]
 >If you are not the resource owner, you will need the following role-based access controls: 1. *Cognitive Services OpenAI User*: This role allows viewing resources and using the chat playground. 2. *Cognitive Services OpenAI Contributor*: This role allows the user to create new deployments.

learn-pr/wwl-data-ai/develop-applications-openai/includes/3-integrate-app.md

@@ -1,4 +1,4 @@
-Azure OpenAI offers both language specific SDKs and a REST API that developers can use to add AI functionality to their applications. Generative AI capabilities in Azure OpenAI are provided through *models*. The models available in the Azure OpenAI service belong to different families, each with their own focus. To use one of these models, you need to deploy through the Azure OpenAI Service.
+Azure OpenAI offers both language specific SDKs and a REST API that developers can use to add AI functionality to their applications. Generative AI capabilities in Azure OpenAI are provided through *models*. The models available in Azure OpenAI belong to different families, each with their own focus. To use one of these models, you need to deploy through Azure OpenAI.
 
 Once you have created an Azure OpenAI resource and deployed a model, you can configure your app.