Merge pull request #5259 from MicrosoftDocs/main

5/28/2025 PM Publish

Author: Taojunshen

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

@@ -5,7 +5,7 @@ description: Learn about content safety for models deployed using standard deplo
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: conceptual
-ms.date: 02/04/2025
+ms.date: 05/19/2025
 ms.author: mopeakande 
 author: msakande
 ms.reviewer: ositanachi

articles/ai-foundry/how-to/configure-managed-network.md

@@ -104,7 +104,7 @@ Before following the steps in this article, make sure you have the following pre
 
 # [Python SDK](#tab/python)
 
-* An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
+* An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version](https://azure.microsoft.com/free/).
 
 * The __Microsoft.Network__ resource provider must be registered for your Azure subscription. This resource provider is used by hub when creating private endpoints for the managed virtual network.
 
@@ -563,7 +563,7 @@ To configure a managed virtual network that allows only approved outbound commun
 
 * __Update an existing hub__:
 
-    The following example demonstrates how to create a managed virtual network for an existing Azure Machine Learning hub named `myhub`. The example also adds several outbound rules for the managed virtual network:
+    The following example demonstrates how to create a managed virtual network for an existing hub named `myhub`. The example also adds several outbound rules for the managed virtual network:
 
     * `myrule` - Adds a private endpoint for an Azure Blob store.
     * `datafactory` - Adds a service tag rule to communicate with Azure Data Factory.
@@ -857,7 +857,7 @@ A private endpoint is automatically created for a connection if the target resou
 
 ### Approval of Private Endpoints
 
-To establish Private Endpoint connections in managed virtual networks using Azure AI Foundry, the workspace managed identity, whether system-assigned or user-assigned, must have permissions to approve the Private Endpoint connections on the target resources. Previously, this was done through automatic role assignments by the Azure AI Foundry service. However, there are security concerns about the automatic role assignment. To improve security, starting April 30th, 2025, we will discontinue this automatic permission grant logic. We recommend assigning the [Azure AI Enterprise Network Connection Approver role](/azure/role-based-access-control/built-in-roles/ai-machine-learning) or a custom role with the necessary Private Endpoint connection permissions on the target resource types and grant this role to the Azure Machine Learning workspace's managed identity to allow Azure AI Foundry services to approve Private Endpoint connections to the target Azure resources.
+To establish Private Endpoint connections in managed virtual networks using Azure AI Foundry, the workspace managed identity, whether system-assigned or user-assigned, must have permissions to approve the Private Endpoint connections on the target resources. Previously, this was done through automatic role assignments by the Azure AI Foundry service. However, there are security concerns about the automatic role assignment. To improve security, starting April 30th, 2025, we will discontinue this automatic permission grant logic. We recommend assigning the [Azure AI Enterprise Network Connection Approver role](/azure/role-based-access-control/built-in-roles/ai-machine-learning) or a custom role with the necessary Private Endpoint connection permissions on the target resource types and grant this role to the Foundry hub's managed identity to allow Azure AI Foundry services to approve Private Endpoint connections to the target Azure resources.
 
 Here's the list of private endpoint target resource types covered by covered by the Azure AI Enterprise Network Connection Approver role:
 

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

@@ -5,7 +5,7 @@ description: Learn how to use Gretel Navigator chat model with Azure AI Foundry.
 ms.service: azure-ai-foundry
 manager: scottpolly
 ms.topic: how-to
-ms.date: 01/13/2025
+ms.date: 05/19/2025
 ms.reviewer: anupamawal
 reviewer: anupamawalaus
 ms.author: mopeakande

articles/ai-foundry/how-to/deploy-models-mistral-open.md

@@ -5,7 +5,7 @@ description: Learn how to use Mistral-7B and Mixtral chat models with Azure AI F
 ms.service: azure-ai-foundry
 manager: scottpolly
 ms.topic: how-to
-ms.date: 01/31/2025
+ms.date: 05/19/2025
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -21,7 +21,7 @@ zone_pivot_groups: azure-ai-model-catalog-samples-chat
 In this article, you learn about Mistral-7B and Mixtral chat models and how to use them.
 Mistral AI offers two categories of models, namely:
 
-- _Premium models_: These include [Mistral Large, Mistral Small, and Ministral 3B](deploy-models-mistral.md) models, and are available as standard deployments with Standard billing.  
+- _Premium models_: These include [Mistral Large, Mistral Small, Mistral-OCR-2503, Mistral Medium 3 (25.05), and Ministral 3B models](deploy-models-mistral.md) models, and are available as standard deployments with Standard billing.  
 - _Open models_: These include [Codestral](deploy-models-mistral-codestral.md) and [Mistral Nemo](deploy-models-mistral-nemo.md) (that are available as standard deployments), and Mixtral-8x7B-Instruct-v01, Mixtral-8x7B-v01, Mistral-7B-Instruct-v01, and Mistral-7B-v01 (that are available to download and run on self-hosted managed endpoints).
 
 

articles/ai-foundry/how-to/deploy-models-openai.md

@@ -10,7 +10,7 @@ ms.custom:
   - ai-learning-hub
   - ignite-2024
 ms.topic: how-to
-ms.date: 11/05/2024
+ms.date: 05/19/2025
 ms.reviewer: fasantia
 ms.author: mopeakande
 author: msakande

articles/ai-foundry/how-to/deploy-models-serverless-connect.md

@@ -5,7 +5,7 @@ description: Learn how to consume standard deployments from a different project
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: how-to
-ms.date: 12/20/2024
+ms.date: 05/19/2025
 ms.author: mopeakande
 author: msakande
 ms.reviewer: fasantia

articles/ai-foundry/includes/content-safety-serverless-apis-note.md

@@ -5,12 +5,9 @@ author: msakande
 ms.author: mopeakande
 ms.service: azure-ai-foundry
 ms.topic: include
-ms.date: 02/04/2025
+ms.date: 05/28/2025
 ms.custom: include file
 ---
 
-@@ -10,4 +10,4 @@ ms.custom: include file
----
-
 > [!NOTE]
 > Azure AI Content Safety is currently available for models deployed as standard deployment, but not to models deployed via managed compute. To learn more about Azure AI Content Safety for models deployed as standard deployment, see [Guardrails & controls for Models Sold Directly by Azure ](../concepts/model-catalog-content-safety.md).

articles/ai-foundry/includes/create-content-filter.md

@@ -35,7 +35,7 @@ Follow these steps to create a content filter:
 
     Now you can configure the input filters (for user prompts) and output filters (for model completion). 
 1. On the **Input filters** page, you can set the filter for the input prompt. For the first four content categories there are three severity levels that are configurable: Low, medium, and high. You can use the sliders to set the severity threshold if you determine that your application or usage scenario requires different filtering than the default values. 
-    Some filters, such as Prompt Shields and Protected material detection, enable you to determine if the model should annotate and/or block content. Selecting **Annotate only** runs the respective model and return annotations via API response, but it will not filter content. In addition to annotate, you can also choose to block content.
+    Some filters, such as Prompt Shields and Protected material detection, enable you to determine if the model should annotate and/or block content. Selecting **Annotate only** runs the respective model and returns annotations via API response, but it will not filter content. In addition to annotate, you can also choose to block content.
 
     If your use case was approved for modified content filters, you receive full control over content filtering configurations and can choose to turn filtering partially or fully off, or enable annotate only for the content harms categories (violence, hate, sexual and self-harm).
 
@@ -80,4 +80,4 @@ Follow these steps to apply a content filter to a deployment:
 Now, you can go to the playground to test whether the content filter works as expected.
 
 > [!TIP]
-> You can also create and update content filters using the REST APIs. For more information, see the [API reference](/rest/api/aiservices/accountmanagement/rai-policies/create-or-update). Content filters can be configured at the resource level. Once a new configuration is created, it can be associated with one or more deployments. For more information about model deployment, see the resource [deployment guide](../../ai-services/openai/how-to/create-resource.md). 
+> You can also create and update content filters using the REST APIs. For more information, see the [API reference](/rest/api/aiservices/accountmanagement/rai-policies/create-or-update). Content filters can be configured at the resource level. Once a new configuration is created, it can be associated with one or more deployments. For more information about model deployment, see the resource [deployment guide](../../ai-services/openai/how-to/create-resource.md). 

articles/ai-foundry/model-inference/concepts/content-filter.md

@@ -2,13 +2,15 @@
 title: Content filtering for Azure AI Foundry Models
 titleSuffix: Azure AI Foundry
 description: Learn about the content filtering capabilities of Azure AI Foundry Models.
-author: santiagxf
-ms.author: fasantia
+author: msakande
+ms.author: mopeakande
 ms.service: azure-ai-model-inference
-ms.topic: conceptual 
-ms.date: 1/21/2025
+ms.topic: conceptual
+ms.date: 05/19/2025
 ms.custom: ignite-2024, github-universe-2024
-manager: nitinme
+manager: scottpolly
+ms.reviewer: fasantia
+reviewer: santiagxf
 ---
 
 

articles/ai-foundry/model-inference/concepts/default-safety-policies.md

@@ -2,12 +2,14 @@
 title: Default Guardrails & controls policies for Azure AI Foundry Models
 titleSuffix: Azure AI Foundry
 description: Learn about the default Guardrails & controls policies that Azure AI Foundry Models uses to flag content.
-author: PatrickFarley
-ms.author: fasantia
+author: msakande
+ms.author: mopeakande
 ms.service: azure-ai-model-inference
-ms.topic: conceptual 
-ms.date: 1/21/2025
-manager: nitinme
+ms.topic: conceptual
+ms.date: 05/19/2025
+manager: scottpolly
+ms.reviewer: fasantia
+reviewer: santiagxf
 ---
 
 # Default Guardrails & controls policies for Azure AI Foundry Models