Merge pull request #2605 from MicrosoftDocs/main

1/29 OOB Publish at 1PM

Author: huypub

articles/ai-foundry/model-inference/breadcrumb/toc.yml

@@ -2,10 +2,10 @@
   tocHref: /azure/
   topicHref: /azure/index
   items:
-  - name: Azure AI services
-    tocHref: /azure/ai-services/
-    topicHref: /azure/ai-services/index
+  - name: AI Foundry
+    tocHref: /azure/ai-foundry/
+    topicHref: /azure/ai-studio/index
     items:
-    - name: Azure AI Model Inference
-      tocHref: /azure/ai-foundry/
+    - name: Model Inference
+      tocHref: /azure/ai-foundry/model-inference/
       topicHref: /azure/ai-foundry/model-inference/index

articles/ai-foundry/model-inference/concepts/models.md

@@ -17,6 +17,9 @@ ms.custom: references_regions, tool_generated
 
 Azure AI model inference in Azure AI Foundry gives you access to flagship models in Azure AI to consume them as APIs without hosting them on your infrastructure.
 
+> [!TIP]
+> DeepSeek-R1 is available for deployment as [Serverless API endpoint](../../../ai-studio/how-to/deploy-models-deepseek.md).
+
 :::image type="content" source="../media/models/models-catalog.gif" alt-text="An animation showing Azure AI studio model catalog section and the models available." lightbox="../media/models/models-catalog.gif":::
 
 Model availability varies by model provider, deployment SKU, and cloud. All models available in Azure AI Model Inference support the [Global standard](deployment-types.md#global-standard) deployment type which uses global capacity to guarantee throughput. [Azure OpenAI models](#azure-openai) also support regional deployments and [sovereign clouds](/entra/identity-platform/authentication-national-cloud)—Azure Government, Azure Germany, and Azure China 21Vianet.

articles/ai-foundry/model-inference/overview.md

@@ -18,6 +18,9 @@ recommendations: false
 
 Azure AI model inference provides access to the most powerful models available in the Azure AI model catalog. The models come from key model providers in the industry, including OpenAI, Microsoft, Meta, Mistral, Cohere, G42, and AI21 Labs. These models can be integrated with software solutions to deliver a wide range of tasks that include content generation, summarization, image understanding, semantic search, and code generation.
 
+> [!TIP]
+> DeepSeek-R1 is available for deployment as [Serverless API endpoint](../../ai-studio/how-to/deploy-models-deepseek.md).
+
 Azure AI model inference provides a way to **consume models as APIs without hosting them on your infrastructure**. Models are hosted in a Microsoft-managed infrastructure, which enables API-based access to the model provider's model. API-based access can dramatically reduce the cost of accessing a model and simplify the provisioning experience.
 
 Azure AI model inference is part of Azure AI Services, and users can access the service through [REST APIs](./reference/reference-model-inference-api.md), [SDKs in several languages](supported-languages.md) such as Python, C#, JavaScript, and Java. You can also use the Azure AI model inference from [Azure AI Foundry by configuring a connection](how-to/configure-project-connection.md).

articles/ai-services/agents/concepts/model-region-support.md

@@ -7,7 +7,7 @@ author: aahill
 ms.author: aahi
 ms.service: azure-ai-agent-service
 ms.topic: conceptual
-ms.date: 01/07/2025
+ms.date: 01/29/2025
 ms.custom: azure-ai-agents
 ---
 
@@ -37,9 +37,10 @@ Azure AI Agent Service supports the same models as the chat completions API in A
 
 The Azure AI Agent Service also supports the following models from the Azure AI Foundry model catalog.
 
-* Llama 3.1-70B-instruct
+* Meta-Llama-405B-Instruct
 * Mistral-large-2407
-* Cohere command R+
+* Cohere-command-r-plus
+* Cohere-command-r
 
 To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference it in your agent. 
 

articles/ai-services/openai/how-to/content-filters.md

@@ -20,7 +20,7 @@ The content filtering system integrated into Azure AI Foundry runs alongside the
 
 The default content filtering configuration is set to filter at the medium severity threshold for all four content harms categories for both prompts and completions. That means that content that is detected at severity level medium or high is filtered, while content detected at severity level low or safe is not filtered by the content filters. Learn more about content categories, severity levels, and the behavior of the content filtering system [here](../concepts/content-filter.md). 
 
-Jailbreak risk detection and protected text and code models are optional and off by default. For jailbreak and protected material text and code models, the configurability feature allows all customers to turn the models on and off. The models are by default off and can be turned on per your scenario. Some models are required to be on for certain scenarios to retain coverage under the [Customer Copyright Commitment](/legal/cognitive-services/openai/customer-copyright-commitment?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext).
+Jailbreak risk detection and protected text and code models are optional and on by default. For jailbreak and protected material text and code models, the configurability feature allows all customers to turn the models on and off. The models are by default on and can be turned off per your scenario. Some models are required to be on for certain scenarios to retain coverage under the [Customer Copyright Commitment](/legal/cognitive-services/openai/customer-copyright-commitment?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext).
 
 > [!NOTE]
 > All customers have the ability to modify the content filters and configure the severity thresholds (low, medium, high). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR). At this time, it is not possible to become a managed customer.