Merge pull request #7678 from MicrosoftDocs/main

Auto Publish – main to live - 2025-10-15 17:14 UTC

Author: learn-build-service-prod[bot]

articles/ai-foundry/openai/concepts/content-filter.md

@@ -17,7 +17,8 @@ manager: nitinme
 Azure OpenAI includes a content filtering system that works alongside core models, including image generation models. This system runs both the prompt and completion through a set of classification models designed to detect and prevent the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Variations in API configurations and application design might affect completions and thus filtering behavior.
 
 > [!IMPORTANT]
-> The content filtering system doesn't apply to prompts and completions processed by the audio models such as Whisper in Azure OpenAI in Azure AI Foundry Models. For more information, see [Audio models in Azure OpenAI](models.md?tabs=standard-audio#standard-deployment-regional-models-by-endpoint).
+> The content filtering system applies to all [Models sold directly by Azure](/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure?tabs=global-standard-aoai%2Cstandard-chat-completions%2Cglobal-standard&pivots=azure-openai), except for prompts and completions processed by the audio models such as Whisper. For more information, see [Audio models in Azure OpenAI](models.md?tabs=standard-audio#standard-deployment-regional-models-by-endpoint).
+
 
 In addition to the content filtering system, Azure OpenAI performs monitoring to detect content and behaviors that suggest use of the service in a manner that might violate applicable product terms. For more information about understanding and mitigating risks associated with your application, see the [Transparency Note for Azure OpenAI](/azure/ai-foundry/responsible-ai/openai/transparency-note?tabs=text). For more information about how data is processed for content filtering and abuse monitoring, see [Data, privacy, and security for Azure OpenAI](/azure/ai-foundry/responsible-ai/openai/data-privacy#preventing-abuse-and-harmful-content-generation).  
 

articles/ai-services/custom-vision-service/getting-started-build-a-classifier.md

@@ -14,6 +14,8 @@ keywords: image recognition, image recognition app, custom vision
 
 # Quickstart: Build an image classification model with the Custom Vision portal
 
+[!INCLUDE [custom-vision-retirement](includes/custom-vision-retirement.md)]
+
 This quickstart explains how to use the Custom Vision web portal to create an image classification model. Once you build a model, you can test it with new images and eventually integrate it into your own image recognition app.
 
 ## Prerequisites

articles/ai-services/custom-vision-service/getting-started-improving-your-classifier.md

@@ -14,6 +14,8 @@ ms.author: pafarley
 
 # How to improve your Custom Vision model
 
+[!INCLUDE [custom-vision-retirement](includes/custom-vision-retirement.md)]
+
 In this guide, you learn how to improve the quality of your Custom Vision model. The quality of your [classifier](./getting-started-build-a-classifier.md) or [object detector](./get-started-build-detector.md) depends on the amount, quality, and variety of labeled data you provide and how balanced the overall dataset is. A good model has a balanced training dataset that is representative of what is submitted to it. The process of building such a model is iterative; it's common to take a few rounds of training to reach expected results.
 
 The following is a general pattern to help you train a more accurate model:

articles/ai-services/custom-vision-service/includes/custom-vision-retirement.md

@@ -14,11 +14,11 @@ ms.author: pafarley
 > Microsoft is announcing the planned retirement of the Azure Custom Vision service. Microsoft will provide full support for all existing Azure Custom Vision customers until 9/25/2028. During this support window, customers are encouraged to begin planning and executing their transition to alternative solutions.
 > Depending on your use case, we recommend the following paths for transition:
 > - For creating custom models for both image classification and object detection, **Azure Machine Learning AutoML** offers the ability to train both custom model types using classic machine learning techniques
-> - [Learn more about Azure Machine Learning AutoML]() and explore how it can offer support for custom model training.
+> - [Learn more about Azure Machine Learning AutoML](/azure/machine-learning/concept-automated-ml?view=azureml-api-2) and explore how it can offer support for custom model training.
 > 
 > Microsoft is also investing in Generative AI-based solutions that increase accuracy in custom scenarios using prompt engineering and other techniques.
 > - To use generative models, you can use one of models available in the Azure AI Foundry model catalog and create your own solution for customized vision.
 > - For a managed generative solution for image classification, Azure AI Content Understanding (currently in public preview) offers the ability to create custom classification workflows. It also supports processing unstructured data of any type (image, documents, audio, video) and extract structured insights based on pre-defined or user-defined formats.
-> - [Learn more about Azure AI Foundry Models]() and [Azure AI Content Understanding (public preview)]() and explore how they can offer alternative paths for your custom needs.
+> - [Learn more about Azure AI Foundry Models](/azure/ai-foundry/concepts/foundry-models-overview) and [Azure AI Content Understanding (public preview)](/azure/ai-services/content-understanding/overview) and explore how they can offer alternative paths for your custom needs.
 >
 > For more detailed guidance on migration, see the [Azure Custom Vision Migration Guide](https://aka.ms/custom-vision-migration).

articles/ai-services/custom-vision-service/limits-and-quotas.md

@@ -13,6 +13,8 @@ ms.author: pafarley
 
 # Limits and quotas
 
+[!INCLUDE [custom-vision-retirement](includes/custom-vision-retirement.md)]
+
 There are two tiers of subscription to the Custom Vision service. You can sign up for an F0 (free) or S0 (standard) subscription through the Azure portal. This page outlines the limitations of each tier. See the [Custom Vision pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/custom-vision-service/) for more details on pricing and transactions.
 
 |Factor|**F0 (free)**|**S0 (standard)**|

articles/ai-services/custom-vision-service/test-your-model.md

@@ -15,6 +15,8 @@ ms.custom: sfi-image-nochange
 
 # Test and retrain a Custom Vision model
 
+[!INCLUDE [custom-vision-retirement](includes/custom-vision-retirement.md)]
+
 After you train your Custom Vision model, you can quickly test it using a locally stored image or a URL pointing to a remote image. Test the most recently trained iteration of your model, and then decide whether further training is needed.
 
 ## Test your model

articles/ai-services/custom-vision-service/use-prediction-api.md

@@ -14,6 +14,8 @@ ms.custom: devx-track-csharp
 
 # Call the Prediction API
 
+[!INCLUDE [custom-vision-retirement](includes/custom-vision-retirement.md)]
+
 After you train your model, you can test it programmatically by submitting images to the Prediction API endpoint. In this guide, you'll learn how to call the Prediction API to score an image. You'll learn the different ways you can configure the behavior of this API to meet your needs.
 
 > [!NOTE]

articles/ai-services/custom-vision-service/whats-new.md

@@ -12,6 +12,8 @@ ms.author: pafarley
 
 # What's new in Custom Vision
 
+[!INCLUDE [custom-vision-retirement](includes/custom-vision-retirement.md)]
+
 Learn what's new in the service. These items may be release notes, videos, blog posts, and other types of information. Bookmark this page to keep up to date with the service.
 
 ## May 2022