Google Professional Data Engineer
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NO.24 You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
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LLeetQuiz
A
Use Cloud Vision AutoML with the existing dataset.
B
Use Cloud Vision AutoML, but reduce your dataset twice.
C
Use Cloud Vision API by providing custom labels as recognition hints.
D
Train your own image recognition model leveraging transfer learning techniques.
Explanation:
Explanation:
Option A is the best choice for this Proof-of-Concept scenario:
- Time constraint - "Within a few working days" requirement makes AutoML ideal
- Dataset size - 750 classes × 1000 examples = 750,000 images - sufficient for AutoML
- No data reduction needed - Option B would reduce model accuracy unnecessarily
- Custom model requirement - Standard Vision API (option C) doesn't support custom component recognition
- Quick deployment - AutoML provides trained model without ML expertise
- Transfer learning - Option D would take significantly longer and require ML expertise
Cloud Vision AutoML:
- Automates the entire ML pipeline
- Handles custom image classification
- Provides REST API for easy integration
- Suitable for the 750-class classification problem
- Meets the rapid POC timeline requirement
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