Merge branch 'main' into quickstart-csharp-fixes

Author: v-dirichards

.github/policies/disallow-edits.yml

@@ -19,14 +19,18 @@ configuration:
                 @${issueAuthor} - You tried to add an index file to this repository; this is not permitted so your pull request will be closed automatically.
           - closePullRequest
 
-      - description: Close PRs to the "ai-services/personalizer" folder where the author isn't a member of the MicrosoftDocs org (i.e. PRs in public repo).
+      - description: Close PRs to the "ai-services/personalizer" and "ai-services/responsible-ai" folders where the author isn't a member of the MicrosoftDocs org (i.e. PRs in public repo).
         if:
           - payloadType: Pull_Request
           - isAction:
               action: Opened
-          - filesMatchPattern:
-              matchAny: true
-              pattern: articles/ai-services/personalizer/*
+          - or:
+            - filesMatchPattern:
+                matchAny: true
+                pattern: articles/ai-services/personalizer/*
+            - filesMatchPattern:
+                matchAny: true
+                pattern: articles/ai-services/responsible-ai/*
           - not:
               activitySenderHasAssociation:
                 association: Member
@@ -65,3 +69,55 @@ configuration:
                 - mrbullwinkle
               replyTemplate: ${mentionees} - Please review this PR and sign off when you're ready to merge it.
               assignMentionees: True # This part probably won't work since the bot doesn't have write perms.
+          - addLabel:
+              label: needs-human-review
+
+      - description: \@mention specific people when a PR is opened in the "ai-services/responsible-ai" folder.
+        if:
+          - payloadType: Pull_Request
+          - isAction:
+              action: Opened
+          - filesMatchPattern:
+              matchAny: true
+              pattern: articles/ai-services/responsible-ai/*
+          - activitySenderHasAssociation:
+              association: Member
+          - not:
+              or:
+                - isActivitySender:
+                    user: eric-urban
+                - isActivitySender:
+                    user: nitinme
+                - isActivitySender:
+                    user: mrbullwinkle
+                - isActivitySender:
+                    user: aahill
+                - isActivitySender:
+                    user: laujan
+                - isActivitySender:
+                    user: patrickfarley
+                - isActivitySender:
+                    user: jboback
+                - isActivitySender:
+                    user: heidisteen
+                - isActivitySender:
+                    user: haileytap
+        then:
+          - addReply:
+              reply: >-
+                @${issueAuthor} - Please don't sign off on this PR. The area owners will sign off once they've reviewed your contribution.
+          - mentionUsers:
+              mentionees:
+                - eric-urban
+                - nitinme
+                - mrbullwinkle
+                - aahill
+                - laujan
+                - patrickfarley
+                - jboback
+                - heidisteen
+                - haileytap
+              replyTemplate: ${mentionees} - Please review this PR and sign off when you're ready to merge it.
+              assignMentionees: True # This part probably won't work since the bot doesn't have write perms.
+          - addLabel:
+              label: needs-human-review

.openpublishing.redirection.json

@@ -259,6 +259,16 @@
       "source_path_from_root": "/articles/open-datasets/dataset-genomics-data-lake.md",
       "redirect_url": "/azure/open-datasets/dataset-catalog",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/openai/concepts/provisioned-reservation-update.md",
+      "redirect_url": "/azure/ai-services/openai/concepts/provisioned-migration",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/custom-vision-service/logo-detector-mobile.md",
+      "redirect_url": "/azure/ai-services/custom-vision-service",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-foundry/.openpublishing.redirection.ai-studio.json

@@ -290,6 +290,11 @@
             "redirect_url": "/azure/ai-foundry/how-to/deploy-stability-models",
             "redirect_document_id": true
           },
+          {
+            "source_path_from_root": "/articles/ai-foundry/how-to/deploy-stability-models.md",
+            "redirect_url": "/azure/ai-foundry/concepts/models-featured#stability-ai",
+            "redirect_document_id": false
+          },          
           {
             "source_path_from_root": "/articles/ai-studio/how-to/disable-local-auth.md",
             "redirect_url": "/azure/ai-foundry/how-to/disable-local-auth",
@@ -1097,6 +1102,11 @@
             "source_path_from_root": "/articles/ai-foundry/model-inference/reference/reference-model-inference-images-embeddings.md",
             "redirect_url": "/rest/api/aifoundry/model-inference/get-image-embeddings/get-image-embeddings",
             "redirect_document_id": false
-          }
+          },
+          {
+            "source_path_from_root": "/articles/ai-foundry/how-to/prompt-flow.md",
+            "redirect_url": "/azure/ai-foundry/concepts/prompt-flow",
+            "redirect_document_id": true
+        }
     ]
 }

articles/ai-foundry/concepts/deployments-overview.md

@@ -4,37 +4,39 @@ titleSuffix: Azure AI Foundry
 description: Learn about deploying models in Azure AI Foundry portal.
 manager: scottpolly
 ms.service: azure-ai-foundry
-ms.custom:
-  - ignite-2023
-  - build-2024
-  - ignite-2024
 ms.topic: concept-article
-ms.date: 10/21/2024
+ms.date: 03/24/2025
 ms.reviewer: fasantia
 ms.author: mopeakande
 author: msakande
 ---
 
 # Overview: Deploy AI models in Azure AI Foundry portal
 
-The model catalog in Azure AI Foundry portal is the hub to discover and use a wide range of models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. The process of interacting with a deployed model is called *inferencing*. Azure AI Foundry offer a comprehensive suite of deployment options for those models depending on your needs and model requirements.
+The model catalog in Azure AI Foundry portal is the hub to discover and use a wide range of models for building generative AI applications. Models need to be deployed to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for models, depending on your needs and model requirements.
 
 ## Deploying models
 
-Deployment options vary depending on the model type:
+Deployment options vary depending on the model offering:
 
-* **Azure OpenAI models:** The latest OpenAI models that have enterprise features from Azure.
-* **Models as a Service models:** These models don't require compute quota from your subscription. This option allows you to deploy your Model as a Service (MaaS). You use a serverless API deployment and are billed per token in a pay-as-you-go fashion.
-* **Open and custom models:** The model catalog offers access to a large variety of models across modalities that are of open access. You can host open models in your own subscription with a managed infrastructure, virtual machines, and the number of instances for capacity management. There's a wide range of models from Azure OpenAI, Hugging Face, and NVIDIA.
+* **Azure OpenAI models:** The latest OpenAI models that have enterprise features from Azure with flexible billing options.
+* **Models-as-a-Service models:** These models don't require compute quota from your subscription and are billed per token in a pay-as-you-go fashion. 
+* **Open and custom models:** The model catalog offers access to a large variety of models across modalities, including models of open access. You can host open models in your own subscription with a managed infrastructure, virtual machines, and the number of instances for capacity management.
 
 Azure AI Foundry offers four different deployment options:
 
 |Name                           | Azure OpenAI service | Azure AI model inference | Serverless API | Managed compute |
 |-------------------------------|----------------------|-------------------|----------------|-----------------|
-| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Models as a Service](../../ai-foundry/model-inference/concepts/models.md) | [Models as a Service](../how-to/model-catalog-overview.md#content-safety-for-models-deployed-via-serverless-apis) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
+| Which models can be deployed? | [Azure OpenAI models](../../ai-services/openai/concepts/models.md)        | [Azure OpenAI models and Models-as-a-Service](../../ai-foundry/model-inference/concepts/models.md) | [Models-as-a-Service](../how-to/model-catalog-overview.md#content-safety-for-models-deployed-via-serverless-apis) | [Open and custom models](../how-to/model-catalog-overview.md#availability-of-models-for-deployment-as-managed-compute) |
 | Deployment resource           | Azure OpenAI resource | Azure AI services resource | AI project resource | AI project resource |
-| Best suited when              | You are planning to use only OpenAI models | You are planning to take advantage of the flagship models in Azure AI catalog, including OpenAI. | You are planning to use a single model from a specific provider (excluding OpenAI). | If you plan to use open models and you have enough compute quota available in your subscription. |
-| Billing bases                 | Token usage & PTU         | Token usage       | Token usage<sup>1</sup>      | Compute core hours<sup>2</sup> |
+| Requires Hubs/Projects        | No | No | Yes | Yes |
+| Data processing options       | Regional <br /> Data-zone  <br /> Global | Global | Regional | Regional |
+| Private networking            | Yes | Yes | Yes | Yes |
+| Content filtering             | Yes | Yes | Yes | No  |
+| Custom content filtering      | Yes | Yes | No  | No  |
+| Key-less authentication       | Yes | Yes | No  | No  |
+| Best suited when              | You're planning to use only OpenAI models | You're planning to take advantage of the flagship models in Azure AI catalog, including OpenAI. | You're planning to use a single model from a specific provider (excluding OpenAI). | If you plan to use open models and you have enough compute quota available in your subscription. |
+| Billing bases                 | Token usage & [provisioned throughput units](../../ai-services/openai/concepts/provisioned-throughput.md)        | Token usage       | Token usage<sup>1</sup>      | Compute core hours<sup>2</sup> |
 | Deployment instructions       | [Deploy to Azure OpenAI Service](../how-to/deploy-models-openai.md) | [Deploy to Azure AI model inference](../model-inference/how-to/create-model-deployments.md) | [Deploy to Serverless API](../how-to/deploy-models-serverless.md) | [Deploy to Managed compute](../how-to/deploy-models-managed.md) |
 
 <sup>1</sup> A minimal endpoint infrastructure is billed per minute. You aren't billed for the infrastructure that hosts the model in pay-as-you-go. After you delete the endpoint, no further charges accrue.
@@ -46,20 +48,18 @@ Azure AI Foundry offers four different deployment options:
 
 ### How should I think about deployment options?
 
-Azure AI Foundry encourages customers to explore the deployment options and pick the one that best suites their business and technical needs. In general you can use the following thinking process:
+Azure AI Foundry encourages you to explore various deployment options and choose the one that best suites your business and technical needs. In general, Consider using the following approach to select a deployment option:
 
-1. Start with the deployment options that have the bigger scopes. This allows you to iterate and prototype faster in your application without having to rebuild your architecture each time you decide to change something. [Azure AI model inference](../../ai-foundry/model-inference/overview.md) is a deployment target that supports all the flagship models in the Azure AI catalog, including latest innovation from Azure OpenAI. To get started, follow [Configure your AI project to use Azure AI model inference](../../ai-foundry/model-inference/how-to/quickstart-ai-project.md).
+* Start with [Azure AI model inference](../../ai-foundry/model-inference/overview.md), which is the option with the largest scope. This option allows you to iterate and prototype faster in your application without having to rebuild your architecture each time you decide to change something. If you're using Azure AI Foundry hubs or projects, enable this option by [turning on the Azure AI model inference feature](../model-inference/how-to/quickstart-ai-project.md#configure-the-project-to-use-azure-ai-model-inference).
 
-2. When you are looking to use a specific model:
+* When you're looking to use a specific model:
 
-   1. When you are interested in Azure OpenAI models, use the Azure OpenAI Service which offers a wide range of capabilities for them and it's designed for them.
+   * If you're interested in Azure OpenAI models, use the Azure OpenAI Service. This option is designed for Azure OpenAI models and offers a wide range of capabilities for them.
 
-   2. When you are interested in a particular model from Models as a Service, and you don't expect to use any other type of model, use [Serverless API endpoints](../how-to/deploy-models-serverless.md). They allow deployment of a single model under a unique set of endpoint URL and keys.
+   * If you're interested in a particular model from Models-as-a-Service, and you don't expect to use any other type of model, use [Serverless API endpoints](../how-to/deploy-models-serverless.md). Serverless endpoints allow deployment of a single model under a unique set of endpoint URL and keys.
 
-3. When your model is not available in Models as a Service and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md) which support deployment of open and custom models. It also allows high level of customization of the deployment inference server, protocols, and detailed configuration.
+* When your model isn't available in Models-as-a-Service and you have compute quota available in your subscription, use [Managed Compute](../how-to/deploy-models-managed.md), which supports deployment of open and custom models. It also allows a high level of customization of the deployment inference server, protocols, and detailed configuration.
 
-> [!TIP]
-> Each deployment option may offer different capabilities in terms of networking, security, and additional features like content safety. Review the documentation for each of them to understand their limitations.
 
 ## Related content
 

articles/ai-foundry/concepts/encryption-keys-portal.md

@@ -87,7 +87,7 @@ Customer-managed key encryption is configured via Azure portal in a similar way
 :::image type="content" source="../../machine-learning/media/concept-customer-managed-keys/cmk-service-side-encryption.png" alt-text="Screenshot of the encryption tab with the option for service side encryption selected." lightbox="../../machine-learning/media/concept-customer-managed-keys/cmk-service-side-encryption.png":::
 
 Alternatively, use infrastructure-as-code options for automation. Example Bicep templates for Azure AI Foundry are available on the Azure Quickstart repo:
-1. [CMK encryption for hub](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-cmk).
+1. [CMK encryption for hub](https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aifoundry-cmk).
 1. [Service-side CMK encryption preview for hub](https://github.com/azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.machinelearningservices/aistudio-cmk-service-side-encryption).
 
 ## Limitations

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

@@ -5,7 +5,7 @@ description: Learn about the lifecycle stages, deprecation, and retirement for m
 manager: scottpolly
 ms.service: azure-ai-foundry
 ms.topic: concept-article
-ms.date: 02/28/2025
+ms.date: 03/20/2025
 ms.author: mopeakande
 author: msakande
 ms.reviewer: kritifaujdar
@@ -98,6 +98,7 @@ The following tables list the timelines for models that are on track for retirem
 
 | Model | Legacy date (UTC) | Deprecation date (UTC) | Retirement date (UTC) | Suggested replacement model |
 |-------|-------------------|------------------------|-----------------------|-----------------------------|
+| [Mistral-small](https://ai.azure.com/explore/models/Mistral-small/version/1/registry/azureml-mistral) | March 31, 2025 | April 30, 2025 | July 31, 2025 | [Mistral-small-2503](https://aka.ms/aistudio/landing/mistral-small-2503) |
 | [Mistral-large-2407](https://aka.ms/azureai/landing/Mistral-Large-2407) | January 13, 2025 | February 13, 2025 | May 13, 2025 | [Mistral-large-2411](https://aka.ms/aistudio/landing/Mistral-Large-2411) |
 | [Mistral-large](https://aka.ms/azureai/landing/Mistral-Large) | December 15, 2024 | January 15, 2025 | April 15, 2025 | [Mistral-large-2411](https://aka.ms/aistudio/landing/Mistral-Large-2411) |