Merge pull request #5683 from msakande/fix-naming-for-azure-ai-model-inference-api

Fix naming for azure ai model inference api

Author: Stacyrch140

articles/ai-foundry/concepts/model-catalog-content-safety.md

@@ -34,7 +34,7 @@ Content filtering occurs synchronously as the service processes prompts to gener
 - When you first deploy a language model
 - Later, by selecting the content filtering toggle on the deployment details page
 
-Suppose you decide to use an API other than the [Foundry Models API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that is deployed via a standard deployment. In such a situation, content filtering (preview) isn't enabled unless you implement it separately by using Azure AI Content Safety. To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). You run a higher risk of exposing users to harmful content if you don't use content filtering (preview) when working with models that are deployed via standard deployments.
+Suppose you decide to use an API other than the [Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) to work with a model that is deployed via a standard deployment. In such a situation, content filtering (preview) isn't enabled unless you implement it separately by using Azure AI Content Safety. To get started with Azure AI Content Safety, see [Quickstart: Analyze text content](/azure/ai-services/content-safety/quickstart-text). You run a higher risk of exposing users to harmful content if you don't use content filtering (preview) when working with models that are deployed via standard deployments.
 
 [!INCLUDE [content-safety-harm-categories](../includes/content-safety-harm-categories.md)]
 

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

@@ -22,7 +22,7 @@ The Azure AI model catalog offers a large selection of Azure AI Foundry Models f
 
 [!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 [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).
+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 [Model Inference 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 Foundry model catalog section and the models available." lightbox="../media/models-featured/models-catalog.gif":::
 
@@ -67,7 +67,7 @@ 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 Foundry Models API.
+The following table lists the Cohere models that you can inference via the Model Inference API.
 
 | Model  | Type | Capabilities | 
 | ------ | ---- | --- | 
@@ -363,7 +363,7 @@ xAI's Grok 3 and Grok 3 Mini models are designed to excel in various enterprise
 
 #### Inference examples: Stability AI
 
-Stability AI models deployed via standard deployment implement the Foundry Models API on the route `/image/generations`.
+Stability AI models deployed via standard deployment implement the Model Inference 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 Foundry Models API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
+In this section, you use the [Azure AI Model Inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> 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.
+> The [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.
 
 ### Create a client to consume the model
 
@@ -235,7 +235,7 @@ result = client.complete(
 
 ### Apply Guardrails and controls
 
-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 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 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 standard deployment doesn't require quota from your subscription
 
 ### A REST client
 
-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:
+Models deployed with the [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:
 
 * 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 [Foundry Models API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
+In this section, you use the [Model Inference API](https://aka.ms/azureai/modelinference) with a chat completions model for chat.
 
 > [!TIP]
-> 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.
+> The [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.
 
 ### 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 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 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 following example shows how to handle events when the model detects harmful content in the input prompt.
 
@@ -536,7 +536,7 @@ For more information on how to track costs, see [Monitor costs for models offere
 
 ## Related content
 
-* [Foundry Models API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md)
+* [Model Inference API](../../ai-foundry/model-inference/reference/reference-model-inference-api.md)
 * [Deploy models as standard deployments](deploy-models-serverless.md)
 * [Consume standard deployments from a different Azure AI Foundry project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in standard deployments](deploy-models-serverless-availability.md)