
Explanation:

Because the existing classifier is an Azure AI Custom Vision project, the lowest-effort workflow is performed directly in the Custom Vision portal , not Azure Machine Learning studio or Vision Studio. Open the existing project so that the new product classes can be incorporated into its current training dataset.
Next, upload representative sample images of each new product. The images should include sufficient variation in viewing angle, lighting, background, size, and presentation to support reliable classification.
Label each image with the appropriate new product tag. Custom Vision uses these tags as the target classes during supervised training. Microsoft's portal workflow explicitly supports uploading and manually tagging images without requiring application code.
Retrain the classifier after the labeled images have been added. Training creates a new iteration based on all current training images and tags, including the newly introduced product categories.
Finally, publish the trained iteration to the prediction resource. Publishing makes that iteration available through the Custom Vision Prediction API used by the application.
Study Guide alignment: create, train, evaluate, retrain, publish, and consume custom image- classification models .
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