A fashion retailer needs to auto-tag thousands of new product photos with detailed, domain-specific attributes such as "cap sleeves" or "paisley pattern." The small analytics team has image examples with correct labels but little machine-learning expertise and wants the quickest path to a high-accuracy model without managing training infrastructure. Which Google Cloud offering best fits these requirements?
Develop and train a bespoke TensorFlow model on Vertex AI Training and deploy it with Vertex AI Prediction.
Call Cloud Vision API's pre-trained label detection to annotate each product image.
Load the images' metadata into BigQuery and create a clustering model with BigQuery ML.
Train a custom image-classification model with Vertex AI AutoML Vision using the retailer's labeled photos.
AutoML Vision lets organizations upload their own labeled images and automatically trains an image-classification model without requiring deep ML knowledge or complex infrastructure management. This matches the retailer's need to recognize custom, domain-specific attributes while keeping effort and expertise requirements low. The pre-trained Cloud Vision API cannot be taught new, specialized labels; it only returns generic categories. Building a fully custom model on Vertex AI with TensorFlow offers maximum flexibility but demands significant ML expertise and operational overhead. BigQuery ML focuses on structured data and does not train image-recognition models, so it is unsuitable for this scenario.
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What is Vertex AI AutoML Vision?
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