Answer-first summary for fast verification
Answer: Use the Cloud Vision API to automatically annotate objects in the images to help specialists with the annotation task.
The question emphasizes minimal up-front cost, which favors pay-per-use services over custom model training. Option B (Cloud Vision API) requires no training data or infrastructure setup, aligning with the cost constraint and providing immediate object detection capabilities. While some community comments (e.g., Omi_04040, AB_C) argue for AutoML (A) due to custom labels or domain specificity, the consensus (e.g., thescientist, vladik820) and higher upvotes for B highlight that Cloud Vision API's pre-trained model avoids training costs and time, making it optimal for scaling with minimal initial investment. Options C (BigQuery ML) is unsuitable for image data, and D (Vertex AI open-source training) incurs training costs, violating the up-front cost requirement.
Author: LeetQuiz Editorial Team
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Your company installs cameras at bridge construction sites that capture hourly images stored in a Cloud Storage bucket. Currently, specialists manually review, filter, and annotate these images. You need to propose a minimal up-front cost ML solution to scale this process and reduce costs. Which method should you propose?
A
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Train an AutoML object detection model to annotate the objects in the images to help specialists with the annotation task.
B
Use the Cloud Vision API to automatically annotate objects in the images to help specialists with the annotation task.
C
Create a BigQuery ML classification model to classify important images. Use the model to predict which new images are important to help specialists with the filtering task.
D
Use Vertex AI to train an open source object detection to annotate the objects in the images to help specialists with the annotation task.