Deprecate model customization feature

PatrickFarley · PatrickFarley · commit f65438b95762 · 2024-09-10T12:22:58.000-04:00
diff --git a/articles/ai-services/computer-vision/concept-model-customization.md b/articles/ai-services/computer-vision/concept-model-customization.md
@@ -14,6 +14,8 @@ ms.author: pafarley
 
 # Model customization (version 4.0 preview)
 
+[!INCLUDE [model-customization-deprecation](includes/model-customization-deprecation.md)]
+
 Model customization lets you train a specialized Image Analysis model for your own use case. Custom models can do either image classification (tags apply to the whole image) or object detection (tags apply to specific areas of the image). Once your custom model is created and trained, it belongs to your Vision resource, and you can call it using the [Analyze Image API](./how-to/call-analyze-image-40.md).
 
 Implement model customization quickly and easily by following a quickstart:
diff --git a/articles/ai-services/computer-vision/how-to/coco-verification.md b/articles/ai-services/computer-vision/how-to/coco-verification.md
@@ -16,6 +16,8 @@ ms.author: pafarley
 
 <!-- nbstart https://raw.githubusercontent.com/Azure-Samples/cognitive-service-vision-model-customization-python-samples/main/docs/check_coco_annotation.ipynb -->
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 > [!TIP]
 > This article is based on the Jupyter notebook _check_coco_annotation.ipynb_. **[Open in GitHub](https://github.com/Azure-Samples/cognitive-service-vision-model-customization-python-samples/blob/main/docs/check_coco_annotation.ipynb)**.
 
diff --git a/articles/ai-services/computer-vision/how-to/migrate-from-custom-vision.md b/articles/ai-services/computer-vision/how-to/migrate-from-custom-vision.md
@@ -14,6 +14,8 @@ ms.author: pafarley
 
 # Migrate a Custom Vision project to Image Analysis 4.0 preview
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 You can migrate an existing Azure AI Custom Vision project to the new Image Analysis 4.0 system. [Custom Vision](../../custom-vision-service/overview.md) is a model customization service that existed before Image Analysis 4.0.
 
 This guide uses Python code to take all of the training data from an existing Custom Vision project (images and their label data) and convert it to a COCO file. You can then import the COCO file into Vision Studio to train a custom Image Analysis model. See [Create and train a custom model](model-customization.md) and go to the section on importing a COCO file&mdash;you can follow the guide from there to the end.
diff --git a/articles/ai-services/computer-vision/how-to/model-customization.md b/articles/ai-services/computer-vision/how-to/model-customization.md
@@ -14,6 +14,8 @@ ms.custom: devx-track-python
 
 # Create a custom Image Analysis model (preview)
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 Image Analysis 4.0 allows you to train a custom model using your own training images. By manually labeling your images, you can train a model to apply custom tags to the images (image classification) or detect custom objects (object detection). Image Analysis 4.0 models are especially effective at few-shot learning, so you can get accurate models with less training data.
 
 This guide shows you how to create and train a custom image classification model. The few differences between training an image classification model and object detection model are noted.
diff --git a/articles/ai-services/computer-vision/how-to/shelf-model-customization.md b/articles/ai-services/computer-vision/how-to/shelf-model-customization.md
@@ -14,6 +14,8 @@ ms.author: pafarley
 
 # Shelf product recognition - custom model (preview)
 
+[!INCLUDE [model-customization-deprecation](../includes/model-customization-deprecation.md)]
+
 You can train a custom model to recognize specific retail products for use in a Product Recognition scenario. The out-of-box [Analyze](shelf-analyze.md) operation doesn't differentiate between products, but you can build this capability into your app through custom labeling and training.
 
 :::image type="content" source="../media/shelf/shelf-analysis-custom.png" alt-text="Photo of a retail shelf with product names and gaps highlighted with rectangles.":::
diff --git a/articles/ai-services/computer-vision/includes/model-customization-deprecation.md b/articles/ai-services/computer-vision/includes/model-customization-deprecation.md
@@ -0,0 +1,14 @@
+---
+title: Model customization deprecation notice
+titleSuffix: Azure AI services
+#services: cognitive-services
+author: PatrickFarley
+manager: nitinme
+ms.service: azure-ai-vision
+ms.topic: include
+ms.date: 09/10/2024
+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.
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)|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) |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)|
