Merge pull request #4704 from sdgilley/sdg-release-merge-conflicts

[DIRTY PR] Merge non-fdp into release branch

Author: rmca14

articles/ai-foundry/at-foundry/ask-at-foundry.md

@@ -20,7 +20,7 @@ author: PatrickFarley
 [Azure AI Foundry](https://ai.azure.com) includes a content filtering system that works alongside core models and image generation models.
 
 > [!IMPORTANT]
-> The content filtering system isn't applied to prompts and completions processed by the Whisper model in Azure OpenAI Service. Learn more about the [Whisper model in Azure OpenAI](../../ai-services/openai/concepts/models.md).
+> The content filtering system isn't applied to prompts and completions processed by the Whisper model in Azure OpenAI in Azure AI Foundry Models. Learn more about the [Whisper model in Azure OpenAI](../../ai-services/openai/concepts/models.md).
 
 ## How it works 
 

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

@@ -19,15 +19,15 @@ The model catalog in Azure AI Foundry portal is the hub to discover and use a wi
 
 Deployment options vary depending on the model offering:
 
-* **Azure OpenAI models:** The latest OpenAI models that have enterprise features from Azure with flexible billing options.
-* **Models-as-a-Service models:** These models don't require compute quota from your subscription and are billed per token in a pay-as-you-go fashion. 
+* **Azure OpenAI in Azure AI Foundry Models:** The latest OpenAI models that have enterprise features from Azure with flexible billing options.
+* **Standard deployment:** These models don't require compute quota from your subscription and are billed per token in a pay-as-you-go fashion. 
 * **Open and custom models:** The model catalog offers access to a large variety of models across modalities, including models of open access. You can host open models in your own subscription with a managed infrastructure, virtual machines, and the number of instances for capacity management.
 
 Azure AI Foundry offers four different deployment options:
 
-|Name                           | Azure OpenAI service | Azure AI model inference | Serverless API | Managed compute |
+|Name                           | Azure OpenAI | Azure AI model inference | Standard deployment | Managed compute |
 |-------------------------------|----------------------|-------------------|----------------|-----------------|
-| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Models-as-a-Service](../../ai-foundry/model-inference/concepts/models.md) | [Models-as-a-Service](../how-to/model-catalog-overview.md#content-safety-for-models-deployed-via-serverless-apis) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
+| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Standard deployment](../../ai-foundry/model-inference/concepts/models.md) | [Standard deployment](../how-to/model-catalog-overview.md#content-safety-for-models-deployed-via-serverless-apis) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
 | Deployment resource           | Azure OpenAI resource | Azure AI services resource | AI project resource | AI project resource |
 | Requires Hubs/Projects        | No | No | Yes | Yes |
 | Data processing options       | Regional <br /> Data-zone  <br /> Global | Global | Regional | Regional |
@@ -37,7 +37,7 @@ Azure AI Foundry offers four different deployment options:
 | Key-less authentication       | Yes | Yes | No  | No  |
 | Best suited when              | You're planning to use only OpenAI models | You're planning to take advantage of the flagship models in Azure AI catalog, including OpenAI. | You're planning to use a single model from a specific provider (excluding OpenAI). | If you plan to use open models and you have enough compute quota available in your subscription. |
 | Billing bases                 | Token usage & [provisioned throughput units](../../ai-services/openai/concepts/provisioned-throughput.md)        | Token usage       | Token usage<sup>1</sup>      | Compute core hours<sup>2</sup> |
-| Deployment instructions       | [Deploy to Azure OpenAI Service](../how-to/deploy-models-openai.md) | [Deploy to Azure AI model inference](../model-inference/how-to/create-model-deployments.md) | [Deploy to Serverless API](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
+| Deployment instructions       | [Deploy to Azure OpenAI](../how-to/deploy-models-openai.md) | [Deploy to Azure AI model inference](../model-inference/how-to/create-model-deployments.md) | [Deploy to Standard deployment](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
 
 <sup>1</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in pay-as-you-go. After you delete the endpoint, no further charges accrue.
 
@@ -54,11 +54,11 @@ Azure AI Foundry encourages you to explore various deployment options and choose
 
 * When you're looking to use a specific model:
 
-   * If you're interested in Azure OpenAI models, use the Azure OpenAI Service. This option is designed for Azure OpenAI models and offers a wide range of capabilities for them.
+   * If you're interested in Azure OpenAI models, use Azure OpenAI in Foundry Models. This option is designed for Azure OpenAI models and offers a wide range of capabilities for them.
 
-   * If you're interested in a particular model from Models-as-a-Service, and you don't expect to use any other type of model, use [Serverless API endpoints](../how-to/deploy-models-serverless.md). Serverless endpoints allow deployment of a single model under a unique set of endpoint URL and keys.
+   * If you're interested in a particular model from serverless pay per token offer, and you don't expect to use any other type of model, use [Standard deployment](../how-to/deploy-models-serverless.md). Standard deployments allow deployment of a single model under a unique set of endpoint URL and keys.
 
-* When your model isn't available in Models-as-a-Service and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md), which supports deployment of open and custom models. It also allows a high level of customization of the deployment inference server, protocols, and detailed configuration.
+* When your model isn't available in standard deployment and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md), which supports deployment of open and custom models. It also allows a high level of customization of the deployment inference server, protocols, and detailed configuration.
 
 
 ## Related content

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

@@ -90,7 +90,7 @@ Fine-tuning is available in specific Azure regions for some models that are depl
 
 For more information on fine-tuning using a managed compute (preview), see [Fine-tune models using managed compute (preview)](../how-to/fine-tune-managed-compute.md).
 
-For details about Azure OpenAI models that are available for fine-tuning, see the [Azure OpenAI Service models documentation](../../ai-services/openai/concepts/models.md#fine-tuning-models) or the [Azure OpenAI models table](#fine-tuning-azure-openai-models) later in this guide.
+For details about Azure OpenAI in Azure AI Foundry Models that are available for fine-tuning, see the [Azure OpenAI in Foundry Models documentation](../../ai-services/openai/concepts/models.md#fine-tuning-models) or the [Azure OpenAI models table](#fine-tuning-azure-openai-models) later in this guide.
 
 For the Azure OpenAI  Service models that you can fine tune, supported regions for fine-tuning include North Central US, Sweden Central, and more.
 

articles/ai-foundry/concepts/fine-tuning-overview.md

@@ -1,11 +1,11 @@
 ---
-title: Deprecation for models in Azure AI model catalog
+title: Deprecation for Foundry Models
 titleSuffix: Azure AI Foundry
-description: Learn about the lifecycle stages, deprecation, and retirement for models in the Azure AI model catalog.
+description: Learn about the lifecycle stages, deprecation, and retirement for Azure AI Foundry Models.
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: concept-article
-ms.date: 03/20/2025
+ms.date: 05/05/2025
 ms.author: mopeakande
 author: msakande
 ms.reviewer: kritifaujdar
@@ -14,15 +14,12 @@ reviewer: fkriti
 #Customer intent: As a data scientist, I want to learn about the lifecycle of models that are available in the model catalog.
 ---
 
-# Model deprecation and retirement in Azure AI model catalog
+# Model deprecation and retirement for Azure AI Foundry Models
 
-Models in the model catalog are continually refreshed with newer and more capable models. As part of this process, model providers might deprecate and retire their older models, and you might need to update your applications to use a newer model. This document communicates information about the model lifecycle and deprecation timelines and explains how you're informed of model lifecycle stages.
+Azure AI Foundry Models in the model catalog are continually refreshed with newer and more capable models. As part of this process, model providers might deprecate and retire their older models, and you might need to update your applications to use a newer model. This document communicates information about the model lifecycle and deprecation timelines and explains how you're informed of model lifecycle stages.
 
 > [!IMPORTANT]
-> This article describes deprecation and retirement only for models that can be deployed to [serverless APIs](../how-to/model-catalog-overview.md#model-deployment-managed-compute-and-serverless-apis) or the [Azure AI model Inference](../../ai-foundry/model-inference/overview.md). This article doesn't cover deprecation information for models that can be deployed only to [managed computes](../how-to/model-catalog-overview.md#managed-compute). 
-
-> [!NOTE]
-> Azure OpenAI models in the model catalog are provided through Azure OpenAI Service. For information about Azure OpenAI model deprecation and retirement, see the [Azure OpenAI service product documentation](/azure/ai-services/openai/concepts/model-retirements).
+> This article describes deprecation and retirement only for Azure Direct models and Azure Ecosystem models models in Foundry Models. For information about deprecation and retirement for Azure OpenAI in Foundry Models, see the Azure OpenAI models lifecycle documentation.
 
 ## Model lifecycle stages
 

articles/ai-foundry/concepts/model-lifecycle-retirement.md

@@ -15,7 +15,7 @@ ms.custom: references_regions, tool_generated
 
 # Featured models of Azure AI Foundry
 
-The Azure AI model catalog offers a large selection of models from a wide range of providers. You have various options for deploying models from the model catalog. This article lists featured models in the model catalog that can be deployed and hosted on Microsoft's servers via serverless APIs. For some of these models, you can also host them on your infrastructure for deployment via managed compute. See [Available models for supported deployment options](../how-to/model-catalog-overview.md#available-models-for-supported-deployment-options) to find models in the catalog that are available for deployment via managed compute or serverless API.
+The Azure AI model catalog offers a large selection of Azure AI Foundry Models from a wide range of providers. You have various options for deploying models from the model catalog. This article lists featured models in the model catalog that can be deployed and hosted on Microsoft's servers via standard deployment. For some of these models, you can also host them on your infrastructure for deployment via managed compute. See [Available models for supported deployment options](../how-to/model-catalog-overview.md#available-models-for-supported-deployment-options) to find models in the catalog that are available for deployment via managed compute or standard deployment.
 
 [!INCLUDE [models-preview](../includes/models-preview.md)]
 
@@ -37,7 +37,7 @@ See [this model collection in Azure AI Foundry portal](https://ai.azure.com/expl
 
 ## Azure OpenAI
 
-Azure OpenAI Service offers a diverse set of models with different capabilities and price points. These models include:
+Azure OpenAI in Foundry Models offers a diverse set of models with different capabilities and price points. These models include:
 
 - State-of-the-art models designed to tackle reasoning and problem-solving tasks with increased focus and capability
 - Models that can understand and generate natural language and code

articles/ai-foundry/concepts/models-featured.md

@@ -57,11 +57,11 @@ sections:
       - question: |
           Can all models be secured with content filtering? 
         answer: |
-          Azure AI Content Safety can be used for AI-generated content from Azure OpenAI Service, open-source, and frontier models. For more information, see [How Azure AI Content Safety helps protect users from the classroom to the chatroom](https://aka.ms/contentsafety_GA_blog).
+          Azure AI Content Safety can be used for AI-generated content from Azure OpenAI in Azure AI Foundry Models, open-source, and frontier models. For more information, see [How Azure AI Content Safety helps protect users from the classroom to the chatroom](https://aka.ms/contentsafety_GA_blog).
       - question: |
           Do you use my company data to train any of the models? 
         answer: |
-          Azure OpenAI Service doesn't use customer data to retrain models. For more information, see the [Azure OpenAI data, privacy, and security guide](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context).    
+          Azure OpenAI doesn't use customer data to retrain models. For more information, see the [Azure OpenAI data, privacy, and security guide](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context).    
   - name: Learning more and where to ask questions
     questions:
       - question: |