GCP Cloud Digital Leader Practice Question

A retail company with limited ML expertise wants to classify product photos into its own 200 product categories, which are more granular than industry-standard labels. How can training a model with AutoML Vision create business value for the company?

It offers real-time inference only on on-premises GPUs, reducing network costs but providing no change in prediction accuracy.
It lets the company train a custom image classification model on its own labeled photos through an easy interface, improving accuracy for its unique categories without needing in-house ML experts.
It automatically labels all images without any manual effort, so the company can train a model without providing labeled data.
It deploys a fixed, generic model that cannot be updated, ensuring consistent predictions across all retailers.

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Answer Description

AutoML Vision allows organizations to upload their own labeled images and, through a no-code or low-code interface, automatically train a custom image-classification model using Google-managed transfer learning and hyper-parameter tuning. Because the model is trained on the retailer's specific categories, prediction accuracy for those categories is typically higher than with a generic pre-trained Vision API. The service removes the need to hire specialized data scientists and accelerates time to production, directly reducing development cost while improving the quality of automated product tagging. The other options are incorrect because AutoML still requires labeled training data, does not lock users into an unchangeable generic model, and is delivered as a managed cloud service rather than guaranteeing on-premises GPU execution.

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