Deprecate model customization feature

PatrickFarley · PatrickFarley · commit a95812f1a2fb · 2024-09-10T13:57:16.000-04:00
diff --git a/articles/ai-services/computer-vision/concept-shelf-analysis.md b/articles/ai-services/computer-vision/concept-shelf-analysis.md
@@ -15,6 +15,8 @@ ms.custom: build-2023, build-2023-dataai
 
 # Product Recognition (version 4.0 preview)
 
+[!INCLUDE [model-customization-deprecation](includes/model-customization-deprecation.md)]
+
 The Product Recognition APIs let you analyze photos of shelves in a retail store. You can detect the presence of products and get their bounding box coordinates. Use it in combination with model customization to train a model to identify your specific products. You can also compare Product Recognition results to your store's planogram document.
 
 Try out the capabilities of Product Recognition quickly and easily in your browser using Vision Studio.
diff --git a/articles/ai-services/computer-vision/how-to/shelf-analyze.md b/articles/ai-services/computer-vision/how-to/shelf-analyze.md
@@ -13,6 +13,8 @@ ms.custom: build-2023, build-2023-dataai
 
 # Shelf Product Recognition (preview): Analyze shelf images using pretrained model
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 The fastest way to start using Product Recognition is to use the built-in pretrained AI models. With the Product Recognition API, you can upload a shelf image and get the locations of products and gaps.
 
 :::image type="content" source="../media/shelf/shelf-analysis-pretrained.png" alt-text="Photo of a retail shelf with products and gaps highlighted with rectangles.":::
diff --git a/articles/ai-services/computer-vision/how-to/shelf-modify-images.md b/articles/ai-services/computer-vision/how-to/shelf-modify-images.md
@@ -13,6 +13,8 @@ ms.custom: build-2023
 
 # Shelf image composition (preview)
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 Part of the Product Recognition workflow involves fixing and modifying the input images so the service can perform correctly. 
 
 This guide shows you how to use the **Stitching API** to combine multiple images of the same physical shelf: this gives you a composite image of the entire retail shelf, even if it's only viewed partially by multiple different cameras.
diff --git a/articles/ai-services/computer-vision/includes/model-customization-deprecation.md b/articles/ai-services/computer-vision/includes/model-customization-deprecation.md
@@ -11,4 +11,6 @@ ms.author: pafarley
 ---
 
 > [!IMPORTANT]
-> The Azure AI Vision model customization feature will be deprecated on January 10, 2025. After this date, it will no longer be supported.
+> This feature is now deprecated. On January 10, 2025, the Azure AI Vision Product Recognition and model customization features will be retired: after this date, API calls to these services will fail.
+>
+> To maintain a smooth operation of your models, transition to [Azure AI Custom Vision](/azure/ai-services/Custom-Vision-Service/overview), which is now generally available. Custom Vision offers similar functionality to these retiring features.
diff --git a/articles/ai-services/computer-vision/overview-image-analysis.md b/articles/ai-services/computer-vision/overview-image-analysis.md
@@ -46,7 +46,7 @@ You can analyze images to provide insights about their visual features and chara
 
 | Name | Description | Concept page |
 |---|---|---|
-|**Model customization** (v4.0 preview only) (approaching deprecation) |You can create and train custom models to do image classification or object detection. Bring your own images, label them with custom tags, and Image Analysis trains a model customized for your use case.|[Model customization](./concept-model-customization.md)|
+|**Model customization** (v4.0 preview only) (deprecated) |You can create and train custom models to do image classification or object detection. Bring your own images, label them with custom tags, and Image Analysis trains a model customized for your use case.|[Model customization](./concept-model-customization.md)|
 |**Read text from images** (v4.0 only)| Version 4.0 preview of Image Analysis offers the ability to extract readable text from images. Compared with the async Computer Vision 3.2 Read API, the new version offers the familiar Read OCR engine in a unified performance-enhanced synchronous API that makes it easy to get OCR along with other insights in a single API call. |[OCR for images](concept-ocr.md)|
 |**Detect people in images** (v4.0 only)|Version 4.0 of Image Analysis offers the ability to detect people appearing in images. The bounding box coordinates of each detected person are returned, along with a confidence score. |[People detection](concept-people-detection.md)|
 |**Generate image captions** | Generate a caption of an image in human-readable language, using complete sentences. Computer Vision's algorithms generate captions based on the objects identified in the image. <br/><br/>The version 4.0 image captioning model is a more advanced implementation and works with a wider range of input images. It's only available in the certain geographic regions. See [Region availability](#region-availability). <br/><br/>Version 4.0 also lets you use dense captioning, which generates detailed captions for individual objects that are found in the image. The API returns the bounding box coordinates (in pixels) of each object found in the image, plus a caption. You can use this functionality to generate descriptions of separate parts of an image.<br/><br/>:::image type="content" source="Images/description.png" alt-text="Photo of cows with a simple description on the right.":::| [Generate image captions (v3.2)](concept-describing-images.md)<br/>[(v4.0)](concept-describe-images-40.md)|
@@ -64,7 +64,9 @@ You can analyze images to provide insights about their visual features and chara
 > [!TIP]
 > You can leverage the Read text and Object detection features of Image Analysis through the [Azure OpenAI](/azure/ai-services/openai/overview) service. The **GPT-4 Turbo with Vision** model lets you chat with an AI assistant that can analyze the images you share, and the Vision Enhancement option uses Image Analysis to give the AI assistant more details about the image (readable text and object locations). For more information, see the [GPT-4 Turbo with Vision quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
-## Product Recognition (v4.0 preview only)
+## Product Recognition (v4.0 preview only) (deprecated)
+
+[!INCLUDE [model-customization-deprecation](includes/model-customization-deprecation.md)]
 
 The Product Recognition APIs let you analyze photos of shelves in a retail store. You can detect the presence or absence of products and get their bounding box coordinates. Use it in combination with model customization to train a model to identify your specific products. You can also compare Product Recognition results to your store's planogram document.
 
diff --git a/articles/ai-services/computer-vision/whats-new.md b/articles/ai-services/computer-vision/whats-new.md
@@ -18,6 +18,14 @@ ms.author: pafarley
 
 Learn what's new in Azure AI Vision. Check this page to stay up to date with new features, enhancements, fixes, and documentation updates. 
 
+## September 2024
+
+### Model customization and Product Recognition deprecation
+
+On January 10, 2025, the Azure AI Vision Product Recognition and model customization features will be retired: after this date, API calls to these services will fail.
+
+To maintain a smooth operation of your models, transition to [Azure AI Custom Vision](/azure/ai-services/Custom-Vision-Service/overview), which is now generally available. Custom Vision offers similar functionality to these retiring features.
+
 ## August 2024
 
 ### New detectable Face attributes
