Topic 1 Question 297
You work for an ecommerce company that wants to automatically classify products in images to improve user experience. You have a substantial dataset of labeled images depicting various unique products. You need to implement a solution for identifying custom products that is scalable, effective, and can be rapidly deployed. What should you do?
Develop a rule-based system to categorize the images.
Use a TensorFlow deep learning model that is trained on the image dataset.
Use a pre-trained object detection model from Model Garden.
Use AutoML Vision to train a model using the image dataset.
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- 正解だと思う選択肢: D
Best Answer: D. Use AutoML Vision to train a model using the image dataset. Why Option D? Fastest and Easiest Deployment
AutoML Vision allows you to train a custom image classification model without deep ML expertise. Automatically optimizes model architecture, feature extraction, and hyperparameters. Fully managed and deployable via Vertex AI with minimal effort. Scalable and Effective for Custom Product Recognition
Since the dataset contains unique products, a custom-trained model is necessary (pre-trained models won’t work well for proprietary products). AutoML Vision scales easily and can handle large image datasets efficiently. Built-in Model Training, Evaluation, and Deployment
Provides an end-to-end ML pipeline, including: Data ingestion (upload labeled images). Automated training and hyperparameter tuning. Easy model deployment and monitoring via Vertex AI.
👍 1tk7867862025/02/19
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