more fixes

Author: msakande

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

@@ -19,7 +19,7 @@ reviewer: fkriti
 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 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.
+> 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](../../ai-services/openai/concepts/model-retirements.md?context=/azure/ai-foundry/context/context) documentation.
 
 ## Model lifecycle stages
 
@@ -58,7 +58,7 @@ Models labeled _Retired_ are no longer available for use. You can't create new d
 
 - Models are labeled _Deprecated_ and remain in the deprecated state for at least 90 days before being moved to the retired state. During this notification period, you can migrate any existing deployments to newer or replacement models.
 
-- For each subscription that has a model deployed as a standard deployment or deployed to the Azure AI model inference, members of the _owner_, _contributor_, _reader_, monitoring contributor_, and _monitoring reader_ roles receive a notification when a model deprecation is announced. The notification contains the dates when the model enters legacy, deprecated, and retired states. The notification might provide information about possible replacement model options, if applicable.
+- For each subscription that has a model deployed as a standard deployment or deployed in Foundry Models, members of the _owner_, _contributor_, _reader_, monitoring contributor_, and _monitoring reader_ roles receive a notification when a model deprecation is announced. The notification contains the dates when the model enters legacy, deprecated, and retired states. The notification might provide information about possible replacement model options, if applicable.
 
 The following tables list the timelines for models that are on track for retirement. The specified dates are in UTC time.
 

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

@@ -1,25 +1,25 @@
 ---
-title: Featured models of Azure AI Foundry
+title: Models available for standard deployment in Azure AI Foundry
 titleSuffix: Azure AI Foundry
-description: Explore various models available within Azure AI Foundry.
+description: Explore various models available for standard deployment in Azure AI Foundry.
 manager: scottpolly
 author: msakande
 reviewer: santiagxf
-ms.service: azure-ai-model-inference
+ms.service: azure-ai-foundry
 ms.topic: conceptual
 ms.date: 03/06/2025
 ms.author: mopeakande
 ms.reviewer: fasantia
-ms.custom: references_regions, tool_generated
+ms.custom: references_regions
 ---
 
 # Featured models of Azure AI Foundry
 
-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.
+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 Azure AI Foundry Models that  can be deployed via standard deployment. For some of these models, you can also host them on your infrastructure for deployment via managed compute.
 
 [!INCLUDE [models-preview](../includes/models-preview.md)]
 
-To perform inferencing with the models, some models such as [Nixtla's TimeGEN-1](#nixtla) and [Cohere rerank](#cohere-rerank) require you to use custom APIs from the model providers. Others support inferencing using the [Azure AI model inference](../model-inference/overview.md). You can find more details about individual models by reviewing their model cards in the [model catalog for Azure AI Foundry portal](https://ai.azure.com/explore/models).
+To perform inferencing with the models, some models such as [Nixtla's TimeGEN-1](#nixtla) and [Cohere rerank](#cohere-rerank) require you to use custom APIs from the model providers. Others support inferencing using the [Foundry Models API](../model-inference/overview.md). You can find more details about individual models by reviewing their model cards in the [model catalog for Azure AI Foundry portal](https://ai.azure.com/explore/models).
 
 :::image type="content" source="../media/models-featured/models-catalog.gif" alt-text="An animation showing Azure AI studio model catalog section and the models available." lightbox="../media/models-featured/models-catalog.gif":::
 
@@ -64,7 +64,7 @@ The Cohere family of models includes various models optimized for different use
 
 ### Cohere command and embed
 
-The following table lists the Cohere models that you can inference via the  Azure AI model Inference.
+The following table lists the Cohere models that you can inference via the Foundry Models API.
 
 | Model  | Type | Capabilities | 
 | ------ | ---- | --- | 
@@ -151,7 +151,7 @@ DeepSeek family of models includes DeepSeek-R1, which excels at reasoning tasks
 | [DeepSeek-V3](https://ai.azure.com/explore/models/deepseek-v3/version/1/registry/azureml-deepseek) <br />(Legacy) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br />  - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
 | [DeepSeek-R1](https://ai.azure.com/explore/models/deepseek-r1/version/1/registry/azureml-deepseek) | [chat-completion with reasoning content](../model-inference/how-to/use-chat-reasoning.md?context=/azure/ai-foundry/context/context) | - **Input:** text (163,840 tokens) <br /> - **Output:** text (163,840 tokens) <br />  - **Tool calling:** No <br /> - **Response formats:** Text. |
 
-For a tutorial on DeepSeek-R1, see [Tutorial: Get started with DeepSeek-R1 reasoning model in Azure AI model inference](../model-inference/tutorials/get-started-deepseek-r1.md?context=/azure/ai-foundry/context/context).
+For a tutorial on DeepSeek-R1, see [Tutorial: Get started with DeepSeek-R1 reasoning model in Foundry Models](../model-inference/tutorials/get-started-deepseek-r1.md?context=/azure/ai-foundry/context/context).
 
 See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=deepseek).
 
@@ -350,7 +350,7 @@ The Stability AI collection of image generation models include Stable Image Core
 
 #### Inference examples: Stability AI
 
-Stability AI models deployed to serverless APIs implement the Azure AI model inference API on the route `/image/generations`.
+Stability AI models deployed via standard deployment implement the Foundry Models API on the route `/image/generations`.
 For examples of how to use Stability AI models, see the following examples:
 
 - [Use OpenAI SDK with Stability AI models for text to image requests](https://github.com/Azure/azureml-examples/blob/main/sdk/python/foundation-models/stabilityai/Text_to_Image_openai_library.ipynb)

articles/ai-foundry/how-to/deploy-models-gretel-navigator.md

@@ -80,10 +80,10 @@ Read more about the [Azure AI inference package and reference](https://aka.ms/az
 
 ## Work with chat completions
 
-In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
+In this section, you use the [Azure AI Foundry Models API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Gretel Navigator chat model.
+> The [Foundry Models API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Gretel Navigator chat model.
 
 ### Create a client to consume the model
 
@@ -235,7 +235,7 @@ result = client.complete(
 
 ### Apply Guardrails and controls
 
-The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+The Foundry Models API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 
 The following example shows how to handle events when the model detects harmful content in the input prompt and the filter is enabled.
 
@@ -310,17 +310,17 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip
 
 ### A REST client
 
-Models deployed with the [Azure AI model inference API](https://aka.ms/azureai/modelinference) can be consumed using any REST client. To use the REST client, you need the following prerequisites:
+Models deployed with the [Foundry Models API](https://aka.ms/azureai/modelinference) can be consumed using any REST client. To use the REST client, you need the following prerequisites:
 
 * To construct the requests, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name`` is your unique model deployment host name and `your-azure-region`` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
 
 ## Work with chat completions
 
-In this section, you use the [Azure AI model inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
+In this section, you use the [Foundry Models API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> The [Azure AI model inference API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Gretel Navigator chat model.
+> The [Foundry Models API](https://aka.ms/azureai/modelinference) allows you to talk with most models deployed in Azure AI Foundry portal with the same code and structure, including Gretel Navigator chat model.
 
 ### Create a client to consume the model
 
@@ -479,7 +479,7 @@ The following example request shows other parameters that you can specify in the
 
 ### Apply Guardrails & controls
 
-The Azure AI model inference API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
+The Foundry Models API supports [Azure AI Content Safety](https://aka.ms/azureaicontentsafety). When you use deployments with Azure AI Content Safety turned on, inputs and outputs pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions.
 
 The following example shows how to handle events when the model detects harmful content in the input prompt.
 
@@ -537,7 +537,7 @@ For more information on how to track costs, see [Monitor costs for models offere
 ## Related content
 
 
-* [Azure AI Model Inference API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md)
+* [Foundry Models API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md)
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Consume serverless API endpoints from a different Azure AI Foundry project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)