Merge pull request #2547 from santiagxf/santiagxf-patch-2

Update deployment-types.md

Author: v-dirichards

articles/ai-foundry/model-inference/concepts/deployment-types.md

@@ -31,12 +31,13 @@ To learn more about deployment options for Azure OpenAI models see [Azure OpenAI
 
 Models from third-party model providers with pay-as-you-go billing (collectively called Models-as-a-Service), makes models available in Azure AI model inference under **standard** deployments with a Global processing option (`Global-Standard`). 
 
-Models-as-a-Service offers regional deployment options under [Serverless API endpoints](../../../ai-studio/how-to/deploy-models-serverless.md) in Azure AI Foundry. Prompts and outputs are processed within the geography specified during deployment. However, those deployments can't be accessed using the Azure AI model inference endpoint in Azure AI Services.
-
 ### Global-Standard
 
 Global deployments leverage Azure's global infrastructure to dynamically route traffic to the data center with best availability for each request. Global standard provides the highest default quota and eliminates the need to load balance across multiple resources. Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure location. Learn more about [data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/).
 
+> [!NOTE]
+> Models-as-a-Service offers regional deployment options under [Serverless API endpoints](../../../ai-studio/how-to/deploy-models-serverless.md) in Azure AI Foundry. Prompts and outputs are processed within the geography specified during deployment. However, those deployments can't be accessed using the Azure AI model inference endpoint in Azure AI Services.
+
 ## Control deployment options
 
 Administrators can control which model deployment types are available to their users by using Azure Policies. Learn more about [How to control AI model deployment with custom policies](../../../ai-studio/how-to/custom-policy-model-deployment.md).

articles/ai-foundry/model-inference/faq.yml

@@ -5,7 +5,7 @@ metadata:
   description: Get answers to the most popular questions about Azure AI model inference
   #services: cognitive-services
   manager: nitinme
-  ms.service: azure-ai-models
+  ms.service: azure-ai-model-inference
   ms.topic: faq
   ms.date: 1/21/2025
   ms.author: fasantia

articles/ai-foundry/model-inference/index.yml

@@ -6,7 +6,7 @@ summary: Azure AI model inference provides access to the most powerful models av
 metadata:
   title: Azure AI model inference documentation - Quickstarts, How-to's, API Reference - Azure AI Foundry | Microsoft Docs
   description: Learn how to use flagship models available in the Azure AI model catalog from the key model providers in the industry, including OpenAI, Microsoft, Meta, Mistral, Cohere, G42, and AI21 Labs.
-  ms.service: azure-ai-models
+  ms.service: azure-ai-model-inference
   ms.custom:
   ms.topic: landing-page
   author: mrbullwinkle

articles/search/index.yml

@@ -5,7 +5,7 @@ summary: Information retrieval at scale for vector and text content in tradition
 metadata:
   title: Azure AI Search documentation
   description: Information retrieval at scale for vector and text content in traditional or generative search scenarios.
-  ms.service: service
+  ms.service: azure-ai-search
   ms.custom:
     - ignite-2023
     - ignite-2024