Merge branch 'main' of https://github.com/MicrosoftDocs/azure-ai-docs-pr into deploy-online-endpoints

Author: paulth1

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

@@ -80,6 +80,12 @@ The following tables list the timelines for models that are on track for retirem
 | [Cohere-rerank-v3-english](https://ai.azure.com/explore/models/Cohere-rerank-v3-english/version/1/registry/azureml-cohere) | February 28, 2025 | March 31, 2025 | June 30, 2025 | [Cohere-rerank-v3.5-english](https://ai.azure.com/explore/models/Cohere-rerank-v3.5/version/1/registry/azureml-cohere) |
 | [Cohere-rerank-v3-multilingual](https://ai.azure.com/explore/models/Cohere-rerank-v3-multilingual/version/1/registry/azureml-cohere) | February 28, 2025 | March 31, 2025 | June 30, 2025 | [Cohere-rerank-v3.5-multilingual](https://ai.azure.com/explore/models/Cohere-rerank-v3.5/version/1/registry/azureml-cohere) |
 
+#### DeepSeek
+
+| Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |
+|-------|-------------------|------------------------|-----------------------|-----------------------------|
+| [DeepSeek-V3](https://aka.ms/azureai/landing/DeepSeek-V3) | April 10, 2025 | May 31, 2025 | August 31, 2025 | [DeepSeek-V3-0324](https://aka.ms/azureai/landing/DeepSeek-V3-0324) |
+
 #### Meta
 
 | Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |

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

@@ -141,11 +141,12 @@ For more examples of how to use Jais models, see the following examples:
 
 ## DeepSeek
 
-DeepSeek family of models includes DeepSeek-R1, which excels at reasoning tasks using a step-by-step training process, such as language, scientific reasoning, and coding tasks, and DeepSeek-V3, a Mixture-of-Experts (MoE) language model. 
+DeepSeek family of models includes DeepSeek-R1, which excels at reasoning tasks using a step-by-step training process, such as language, scientific reasoning, and coding tasks, DeepSeek-V3-0324, a Mixture-of-Experts (MoE) language model, and more. 
 
 | Model  | Type | Capabilities | 
 | ------ | ---- | --- | 
-| [DeepSeek-V3](https://ai.azure.com/explore/models/deepseek-v3/version/1/registry/azureml-deepseek) | [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 |
+| [DeekSeek-V3-0324](https://ai.azure.com/explore/models/deepseek-v3-0324/version/1/registry/azureml-deepseek) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (131,072 tokens) <br /> - **Output:** (131,072 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
+| [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).
@@ -171,9 +172,12 @@ Meta Llama models and tools are a collection of pretrained and fine-tuned genera
 - Small language models (SLMs) like 1B and 3B Base and Instruct models for on-device and edge inferencing
 - Mid-size large language models (LLMs) like 7B, 8B, and 70B Base and Instruct models
 - High-performant models like Meta Llama 3.1-405B Instruct for synthetic data generation and distillation use cases.
+- High-performant natively multimodal models, Llama 4 Scout and Llama 4 Maverick, leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
 
 | Model  | Type | Capabilities |
 | ------ | ---- | ------------ |
+| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** Yes <br /> - **Response formats:** Text |
+| [Llama 4-Maverick-17B-128E-Instruct-FP8](https://aka.ms/aifoundry/landing/llama-4-maverick-17b-128e-instruct-fp8) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** Yes <br /> - **Response formats:** Text |
 | [Llama-3.3-70B-Instruct](https://ai.azure.com/explore/models/Llama-3.3-70B-Instruct/version/4/registry/azureml-meta) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.2-90B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-90B-Vision-Instruct/version/1/registry/azureml-meta) | [chat-completion (with images)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.2-11B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-11B-Vision-Instruct/version/1/registry/azureml-meta) | [chat-completion (with images)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |

articles/ai-foundry/includes/region-availability-maas.md

@@ -41,6 +41,7 @@ Cohere Embed v3 -  Multilingual    |  [Microsoft Managed Countries/Regions](/par
 
 | Model | Offer Availability Region  | Hub/Project Region for Deployment  | Hub/Project Region for Fine tuning  |
 |---------|---------|---------|---------|
+DeepSeek-V3-0324                  | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> West US <br> West US 3  | Not available       |
 DeepSeek-V3                       | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> West US <br> West US 3  | Not available       |
 DeepSeek-R1                       | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> West US <br> West US 3  | Not available       |
 

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

@@ -110,7 +110,8 @@ DeepSeek family of models includes DeepSeek-R1, which excels at reasoning tasks
 | Model  | Type | Tier | Capabilities |
 | ------ | ---- | --- | ------------ |
 | [DeekSeek-R1](https://ai.azure.com/explore/models/deepseek-r1/version/1/registry/azureml-deepseek) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | Global standard | - **Input:** text (163,840 tokens) <br /> - **Output:**  (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text. |
-| [DeekSeek-V3](https://ai.azure.com/explore/models/deepseek-v3/version/1/registry/azureml-deepseek) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
+| [DeekSeek-V3](https://ai.azure.com/explore/models/deepseek-v3/version/1/registry/azureml-deepseek) <br />(Legacy) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
+| [DeekSeek-V3-0324](https://ai.azure.com/explore/models/deepseek-v3-0324/version/1/registry/azureml-deepseek) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:**  (131,072 tokens) <br /> - **Languages:** `en` and `zh` <br />  - **Tool calling:** No <br /> - **Response formats:** Text, JSON |
 
 For a tutorial on DeepSeek-R1, see [Tutorial: Get started with DeepSeek-R1 reasoning model in Azure AI model inference](../tutorials/get-started-deepseek-r1.md).
 

articles/ai-foundry/model-inference/includes/create-model-deployments/cli.md

@@ -48,15 +48,16 @@ To add a model, you first need to identify the model that you want to deploy. Yo
     ```azurecli
     accountName="<ai-services-resource-name>"
     resourceGroupName="<resource-group>"
+    location="eastus2"
     ```
 
 3. If you don't have an Azure AI Services account create yet, you can create one as follows:
 
     ```azurecli
-    az cognitiveservices account create -n $accountName -g $resourceGroupName --custom-domain $accountName
+    az cognitiveservices account create -n $accountName -g $resourceGroupName --custom-domain $accountName --location $location --kind AIServices --sku S0
     ```
 
-4. Let's see first which models are available to you and under which SKU. The following command list all the model definitions available:
+4. Let's see first which models are available to you and under which SKU. SKUs, also known as [deployment types](../../concepts/deployment-types.md), define how Azure infrastructure is used to process requests. Models may offer different deployment types. The following command list all the model definitions available:
     
     ```azurecli
     az cognitiveservices account list-models \
@@ -77,10 +78,7 @@ To add a model, you first need to identify the model that you want to deploy. Yo
     }
     ```
 
-6. Identify the model you want to deploy. You need the properties `name`, `format`, `version`, and `sku`. Capacity might also be needed depending on the type of deployment.
-   
-   > [!TIP]
-   > Notice that not all the models are available in all the SKUs.
+6. Identify the model you want to deploy. You need the properties `name`, `format`, `version`, and `sku`. The property `format` indicates the provider offering the model. Capacity might also be needed depending on the type of deployment.
 
 7. Add the model deployment to the resource. The following example adds `Phi-3.5-vision-instruct`:
 

articles/ai-foundry/model-inference/quotas-limits.md

@@ -32,9 +32,9 @@ Azure uses quotas and limits to prevent budget overruns due to fraud, and to hon
 | -------------------- | ------------------- | ----------- |
 | Tokens per minute    | Azure OpenAI models | Varies per model and SKU. See [limits for Azure OpenAI](../../ai-services/openai/quotas-limits.md). |
 | Requests per minute  | Azure OpenAI models | Varies per model and SKU. See [limits for Azure OpenAI](../../ai-services/openai/quotas-limits.md). |
-| Tokens per minute    | DeepSeek-R1         | 5,000,000 |
-| Requests per minute  | DeepSeek-R1         | 5,000     |
-| Concurrent requests  | DeepSeek-R1         | 300       |
+| Tokens per minute    | DeepSeek-R1<br />DeepSeek-V3-0324         | 5,000,000 |
+| Requests per minute  | DeepSeek-R1<br />DeepSeek-V3-0324         | 5,000     |
+| Concurrent requests  | DeepSeek-R1<br />DeepSeek-V3-0324         | 300       |
 | Tokens per minute    | Rest of models      | 400,000   |
 | Requests per minute  | Rest of models      | 1,000     |
 | Concurrent requests  | Rest of models      | 300       |

articles/ai-foundry/toc.yml

@@ -557,8 +557,6 @@ items:
     href: ai-services/content-safety-overview.md
   - name: Content safety for models deployed with serverless APIs
     href: concepts/model-catalog-content-safety.md
-  - name: Use Azure AI Content Safety in AI Foundry portal
-    href: /azure/ai-services/content-safety/how-to/foundry?context=/azure/ai-foundry/context/context
   - name: Content filtering
     href: concepts/content-filtering.md
   - name: Use blocklists

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -160,6 +160,16 @@
       "redirect_url": "/azure/ai-services/content-safety/quickstart-custom-categories",
       "redirect_document_id": true
     },
+    {
+      "source_path_from_root": "/articles/ai-services/content-safety/how-to/foundry.md",
+      "redirect_url": "/azure/ai-foundry/ai-services/content-safety-overview",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/content-safety/studio-quickstart.md",
+      "redirect_url": "/azure/ai-foundry/ai-services/content-safety-overview?context=/azure/ai-services/content-safety/context/context",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/speech-service/how-to-custom-voice-create-voice.md",
       "redirect_url": "/azure/ai-services/speech-service/professional-voice-train-voice",

articles/ai-services/agents/how-to/tools/fabric.md

@@ -34,6 +34,8 @@ You need to first build and publish a Fabric data agent and then connect your Fa
 
 * Developers and end users have at least `READ` access to the Fabric data agent and the underlying data sources it connects with.
 
+* Your Fabric Data Agent and Azure AI Agent need to be in the same tenant.
+
 ## Setup  
 > [!NOTE]
 > * The model you selected in Azure AI Agent setup is only used for agent orchestration and response generation. It doesn't impact which model Fabric data agent uses for NL2SQL operation.