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Answer: Use AutoML Vision to train a model using the image dataset.
The question emphasizes three key requirements: scalability, effectiveness, and rapid deployment, with a substantial labeled dataset available. Option D (AutoML Vision) is optimal because it is specifically designed for custom image classification tasks, leverages Google's infrastructure for scalability, requires minimal ML expertise for deployment, and efficiently uses labeled data to build effective models quickly. Option B (TensorFlow deep learning model) is less suitable as it requires significant development time and expertise, delaying deployment. Option C (pre-trained object detection model) may not align with the custom product classification need, as object detection focuses on localization rather than categorization. Option A (rule-based system) is ineffective for complex image classification and not scalable. The community discussion unanimously supports D, citing AutoML Vision's scalability, deployment speed, and effectiveness with labeled data.
Author: LeetQuiz Editorial Team
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You work for an ecommerce company aiming to automatically classify products in images to enhance user experience. You possess a large dataset of labeled images featuring various unique products. You must implement a scalable, effective solution for identifying custom products that can be deployed rapidly. What should you do?
A
Develop a rule-based system to categorize the images.
B
Use a TensorFlow deep learning model that is trained on the image dataset.
C
Use a pre-trained object detection model from Model Garden.
D
Use AutoML Vision to train a model using the image dataset.