Merge pull request #222 from MicrosoftDocs/main

9/10/2024 PM Publish

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -315,6 +315,11 @@
       "redirect_url": "/azure/ai-services/computer-vision/sdk/install-sdk",
       "redirect_document_id": true
     },
+    {
+      "source_path_from_root": "/articles/ai-services/computer-vision/how-to/migrate-from-custom-vision.md",
+      "redirect_url": "/azure/ai-services/computer-vision/how-to/model-customization",
+      "redirect_document_id": false
+    },
     {
       "source_path_from_root": "/articles/ai-services/document-intelligence/concept-document-intelligence-studio.md",
       "redirect_url": "/azure/ai-services/document-intelligence/studio-overview",

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:

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.

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)**.
 

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.
@@ -350,7 +352,7 @@ The prediction results appear in the right column.
 
 ## Prepare training data
 
-The first thing you need to do is create a COCO file from your training data. You can create a COCO file by converting an old Custom Vision project using the [migration script](migrate-from-custom-vision.md). Or, you can create a COCO file from scratch using some other labeling tool. See the following specification:
+The first thing you need to do is create a COCO file from your training data. See the following specification:
 
 [!INCLUDE [coco-files](../includes/coco-files.md)]
 

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.":::