more rebranding

Author: msakande

articles/ai-foundry/concepts/ai-resources.md

@@ -32,7 +32,7 @@ When a single platform team is responsible for the setup of cloud resources, the
 
 ## Set up and secure a hub for your team
 
-Get started by [creating your first hub in Azure AI Foundry portal](../how-to/create-azure-ai-resource.md), or use [Azure portal](../how-to/create-secure-ai-hub.md) or [templates](../how-to/create-azure-ai-hub-template.md) for advanced configuration options. You can customize networking, identity, encryption, monitoring, or tags, to meet compliance with your organization’s requirements.
+Get started by [creating your first hub in Azure AI Foundry portal](../how-to/create-azure-ai-resource.md), or use [Azure portal](../how-to/create-secure-ai-hub.md) or [templates](../how-to/create-azure-ai-hub-template.md) for advanced configuration options. You can customize networking, identity, encryption, monitoring, or tags, to meet compliance with your organization's requirements.
 
 Often, projects in a business domain require access to the same company resources such as vector indices, model endpoints, or repos. As a team lead, you can preconfigure connectivity with these resources within a hub, so developers can access them from any new project workspace without delay on IT.
 
@@ -76,7 +76,7 @@ Projects also have specific settings that only hold for that project:
 
 ## Azure AI services API access keys
 
-The hub allows you to set up connections to existing Azure OpenAI or Azure AI Services resource types, which can be used to host model deployments. You can access these model deployments from connected resources in Azure AI Foundry portal. Keys to connected resources can be listed from the Azure AI Foundry portal or Azure portal. For more information, see [Find Azure AI Foundry resources in the Azure portal](#find-azure-ai-foundry-resources-in-the-azure-portal).
+The hub allows you to set up connections to existing Azure OpenAI or Azure AI Services resource types, which can be used to host model deployments. You can access these model deployments from connected resources in Azure AI Foundry portal. Keys to connected resources can be listed from the Azure AI Foundry portal or Azure portal. For more information, see [Find Azure AI Foundry Service in the Azure portal](#find-azure-ai-foundry-resources-in-the-azure-portal).
 
 ### Virtual networking
 
@@ -115,7 +115,7 @@ If you require to group costs of these different services together, we recommend
 
 You can use [cost management](/azure/cost-management-billing/costs/quick-acm-cost-analysis) and [Azure resource tags](/azure/azure-resource-manager/management/tag-resources) to help with a detailed resource-level cost breakdown, or run [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator/) on the above listed resources to obtain a pricing estimate. For more information, see [Plan and manage costs for Azure AI services](../how-to/costs-plan-manage.md).
 
-## Find Azure AI Foundry resources in the Azure portal
+## Find Azure AI Foundry Services in the Azure portal
 
 In the Azure portal, you can find resources that correspond to your project in Azure AI Foundry portal.
 

articles/ai-foundry/concepts/architecture.md

@@ -22,15 +22,13 @@ Azure AI Foundry provides a unified experience for AI developers and data scient
 
 At the top level, Azure AI Foundry provides access to the following resources:
 
-<!-- The top level Azure AI Foundry resources (hub and project) are based on Azure Machine Learning. Connected resources, such as Azure OpenAI, Azure AI services, and Azure AI Search, are used by the hub and project in reference, but follow their own resource management lifecycle. -->
-
 - **Azure OpenAI**: Provides access to the latest OpenAI models. You can create secure deployments, try playgrounds, fine tune models, content filters, and batch jobs. The Azure OpenAI resource provider is `Microsoft.CognitiveServices/account` and the kind of resource is `OpenAI`. You can also connect to Azure OpenAI by using a kind of `AIServices`, which also includes other [Azure AI services](/azure/ai-services/what-are-ai-services).
 
     When you use Azure AI Foundry portal, you can directly work with Azure OpenAI without an Azure Studio project. Or you can use Azure OpenAI through a project.
 
     For more information, visit [Azure OpenAI in Azure AI Foundry portal](../azure-openai-in-azure-ai-foundry.md).
 
-- **Management center**: The management center streamlines governance and management of Azure AI Foundry resources such as hubs, projects, connected resources, and deployments.
+- **Management center**: The management center streamlines governance and management of Azure AI Foundry services such as hubs, projects, connected resources, and deployments.
 
     For more information, visit [Management center](management-center.md).
 - **Azure AI Foundry hub**: The hub is the top-level resource in Azure AI Foundry portal, and is based on the Azure Machine Learning service. The Azure resource provider for a hub is `Microsoft.MachineLearningServices/workspaces`, and the kind of resource is `Hub`. It provides the following features:
@@ -48,7 +46,7 @@ At the top level, Azure AI Foundry provides access to the following resources:
     - Project-scoped connections. For example, project members might need private access to data stored in an Azure Storage account without giving that same access to other projects.
     - Open source model deployments from catalog and fine-tuned model endpoints.
 
-    :::image type="content" source="../media/concepts/resource-provider-connected-resources.svg" alt-text="Diagram of the relationship between Azure AI Foundry resources." :::
+    :::image type="content" source="../media/concepts/resource-provider-connected-resources.svg" alt-text="Diagram of the relationship between Azure AI Foundry services." :::
 
     For more information, visit [Hubs and projects overview](ai-resources.md).
 

articles/ai-foundry/how-to/azure-policy.md

@@ -7,7 +7,7 @@ ms.author: larryfr
 ms.date: 01/24/2025
 ms.service: azure-ai-foundry
 ms.topic: how-to
-# Customer Intent: As an admin, I want to understand how I can use Azure Policy to audit and govern Azure AI Foundry resources so that I can ensure compliance with my organization's requirements.
+# Customer Intent: As an admin, I want to understand how I can use Azure Policy to audit and govern Azure AI Foundry Services so that I can ensure compliance with my organization's requirements.
 ---
 
 # Audit and manage Azure AI Foundry hubs and projects

articles/ai-foundry/how-to/costs-plan-manage.md

@@ -104,7 +104,7 @@ You can get to cost analysis from the [Azure portal](https://portal.azure.com).
 > [!IMPORTANT]
 > Your Azure AI Foundry project costs are only a subset of your overall application or solution costs. You need to monitor costs for all Azure resources used in your application or solution. For more information, see Azure AI Foundry hubs.
 
-For the examples in this section, assume that all Azure AI Foundry resources are in the same resource group. But you can have resources in different resource groups. For example, your Azure AI Search resource might be in a different resource group than your project.
+For the examples in this section, assume that all Azure AI Foundry Services are in the same resource group. But you can have resources in different resource groups. For example, your Azure AI Search resource might be in a different resource group than your project.
 
 Here's an example of how to monitor costs for a project. The costs are used as an example only. Your costs vary depending on the services that you use and the amount of usage.
 
@@ -129,7 +129,7 @@ Here's an example of how to monitor costs for a project. The costs are used as a
 
     In this example:
     - The resource group name is **rg-contosoairesource**.
-    - The total cost for all resources and services in the example resource group is **$222.97**. In this example, $222.97 is the total cost for your application or solution that you're building with Azure AI Foundry. Again, this example assumes that all Azure AI Foundry resources are in the same resource group. But you can have resources in different resource groups.
+    - The total cost for all resources and services in the example resource group is **$222.97**. In this example, $222.97 is the total cost for your application or solution that you're building with Azure AI Foundry. Again, this example assumes that all Azure AI Foundry Services are in the same resource group. But you can have resources in different resource groups.
     - The project name is **contoso-outdoor-proj**.
     - The costs that are limited to resources and services in the example [project](../concepts/ai-resources.md#organize-work-in-projects-for-customization) total **$212.06**. 
 
@@ -142,7 +142,7 @@ Here's an example of how to monitor costs for a project. The costs are used as a
 You can also view resource group costs directly from the Azure portal. To do so:
 1. Sign in to [Azure portal](https://portal.azure.com).
 1. Select **Resource groups**. 
-1. Find and select the resource group that contains your Azure AI Foundry resources.
+1. Find and select the resource group that contains your Azure AI Foundry Services.
 1. From the left pane, select **Cost analysis**.
 
     :::image type="content" source="../media/cost-management/project-costs/costs-per-resource-group.png" alt-text="Screenshot of the Azure portal cost analysis at the resource group level." lightbox="../media/cost-management/project-costs/costs-per-resource-group.png":::

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

@@ -557,7 +557,7 @@ In this section, you create an endpoint with the name **meta-llama3-8b-qwerty**.
 
 ## Use the serverless API endpoint
 
-Models deployed in Azure Machine Learning and Azure AI Foundry in Serverless API endpoints support the [Azure AI Model Inference API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md) 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. 
+Models deployed in Azure Machine Learning and Azure AI Foundry in Serverless API endpoints support the [Foundry Models API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md) 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. 
 
 Read more about the [capabilities of this API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md#capabilities) and how [you can use it when building applications](../../ai-foundry/model-inference/reference/reference-model-inference-api.md#getting-started). 
 

articles/ai-foundry/how-to/deploy-nvidia-inference-microservice.md

@@ -31,20 +31,20 @@ NVIDIA inference microservices are containers built by NVIDIA for optimized pret
 
 - Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Foundry portal. To perform the steps in this article, your user account must be assigned a _custom role_ with the following permissions. User accounts assigned the _Owner_ or _Contributor_ role for the Azure subscription can also create NIM deployments. For more information on permissions, see [Role-based access control in Azure AI Foundry portal](../concepts/rbac-azure-ai-foundry.md).
 
-    -	On the Azure subscription—**to subscribe the workspace to the Azure Marketplace offering**, once for each workspace/project:
-        -	Microsoft.MarketplaceOrdering/agreements/offers/plans/read
-        -	Microsoft.MarketplaceOrdering/agreements/offers/plans/sign/action
-        -	Microsoft.MarketplaceOrdering/offerTypes/publishers/offers/plans/agreements/read
-        -	Microsoft.Marketplace/offerTypes/publishers/offers/plans/agreements/read
-        -	Microsoft.SaaS/register/action
-
-    -	On the resource group—**to create and use the SaaS resource**:
+    -    On the Azure subscription—**to subscribe the workspace to the Azure Marketplace offering**, once for each workspace/project:
+        -    Microsoft.MarketplaceOrdering/agreements/offers/plans/read
+        -    Microsoft.MarketplaceOrdering/agreements/offers/plans/sign/action
+        -    Microsoft.MarketplaceOrdering/offerTypes/publishers/offers/plans/agreements/read
+        -    Microsoft.Marketplace/offerTypes/publishers/offers/plans/agreements/read
+        -    Microsoft.SaaS/register/action
+
+    -    On the resource group—**to create and use the SaaS resource**:
         -   Microsoft.SaaS/resources/read
-        -	Microsoft.SaaS/resources/write
+        -    Microsoft.SaaS/resources/write
 
-    -	On the workspace—**to deploy endpoints**:
-        -	Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*
-        -	Microsoft.MachineLearningServices/workspaces/onlineEndpoints/* 
+    -    On the workspace—**to deploy endpoints**:
+        -    Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*
+        -    Microsoft.MachineLearningServices/workspaces/onlineEndpoints/* 
 
 
 ## NVIDIA NIM pay-as-you-go offer on Azure Marketplace by NVIDIA
@@ -81,7 +81,7 @@ After your deployment is successfully created, you can go to **Models + Endpoint
 
 NVIDIA NIMs on Foundry expose an OpenAI compatible API. Learn more about the payload supported [here](https://docs.nvidia.com/nim/large-language-models/latest/api-reference.html#). The 'model' parameter for NIMs on Foundry is set to a default value within the container, and is not required to be passed in the request payload to your online endpoint. The **Consume** tab of the NIM deployment on Foundry includes code samples for inference with the target URL of your deployment. 
 
-You can also consume NIM deployments using the [Azure AI Model Inference SDK](/python/api/overview/azure/ai-inference-readme), with limitations such as no support for [creating and authenticating clients using `load_client`](/python/api/overview/azure/ai-inference-readme#create-and-authenticate-clients-using-load_client) and calling client method `get_model_info` to [retrieve model information](/python/api/overview/azure/ai-inference-readme#get-ai-model-information).
+You can also consume NIM deployments using the [Azure AI Foundry Models SDK](/python/api/overview/azure/ai-inference-readme), with limitations such as no support for [creating and authenticating clients using `load_client`](/python/api/overview/azure/ai-inference-readme#create-and-authenticate-clients-using-load_client) and calling client method `get_model_info` to [retrieve model information](/python/api/overview/azure/ai-inference-readme#get-ai-model-information).
 
 ### Develop and run agents with NIM endpoints
 

articles/ai-foundry/how-to/develop/connections-add-sdk.md

@@ -25,7 +25,7 @@ Connections are a way to authenticate and consume both Microsoft and other resou
 ## Prerequisites
 
 - An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure AI Foundry](https://azure.microsoft.com/free/) today.
-- An Azure AI Foundry hub. For information on creating a hub, see [Create Azure AI Foundry resources with the SDK](./create-hub-project-sdk.md).
+- An Azure AI Foundry hub. For information on creating a hub, see [Create Azure AI Foundry Services with the SDK](./create-hub-project-sdk.md).
 - A resource to create a connection to. For example, an AI Services resource. The examples in this article use placeholders that you must replace with your own values when running the code.
 
 ## Set up your environment

articles/ai-foundry/how-to/develop/get-started-projects-vs-code.md

@@ -100,7 +100,7 @@ The Azure AI Foundry Extension opens in its own view, with the Azure AI Foundry
 
 :::image type="content" source="../../media/how-to/get-started-projects-vs-code/initial-view.png" alt-text="A screenshot of the Azure AI Foundry Extension with highlighted sections.":::
 
-- **Resources**: This section contains the resources you have access to in your Azure AI Foundry project. The **Resources** section is the main view for interacting with your Azure AI Foundry resources. It contains the following subsections:
+- **Resources**: This section contains the resources you have access to in your Azure AI Foundry project. The **Resources** section is the main view for interacting with your Azure AI Foundry Services. It contains the following subsections:
   - **Models**: This section contains the models you can use to build and deploy your AI applications. The **Models** view is where you can find your deployed models in your Azure AI Foundry project.  
   - **Agents**: This section contains your deployed agents in your Azure AI Foundry project.
   - **Threads**: This section contains the threads and runs from a deployed agent in your Azure AI Foundry project.
@@ -315,6 +315,6 @@ Delete the connected tool with the following steps:
 1. Select the Azure Resource Group containing the tool.
 1. Select the **Delete** button.  
 
-##	Next steps
+##    Next steps
 
 - Learn about [working with the Azure AI Foundry Agent Service](./vs-code-agents.md) using this Visual Studio Code extension.