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Answer: Use Cloud Vision AutoML with the existing dataset.
Given the large number of unique components (750) and the substantial dataset (1000 examples per component), along with the tight deadline for creating a proof-of-concept, utilizing Cloud Vision AutoML with the existing dataset is the most practical and efficient choice. Cloud Vision AutoML, a Google Cloud machine learning tool, allows for the quick training of custom image recognition models. Reducing the dataset size (Option C) could impair model accuracy, while relying on custom labels (Option B) may not be universally applicable or accurate. Training a model from scratch using transfer learning (Option A) is time-consuming and not feasible within the given timeframe.
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You work for a manufacturing company that sources 750 different components from 750 suppliers. Your team has a labeled dataset with approximately 1000 examples of each unique component. The goal is to develop an app within a few working days that can help warehouse workers identify incoming components by analyzing photos of them. What is the most efficient approach to create a Proof-Of-Concept for this app?
A
Train your own image recognition model using transfer learning techniques.
B
Use Cloud Vision API and provide custom labels as hints for recognition.
C
Use Cloud Vision AutoML, but reduce the size of the dataset by half.
D
Use Cloud Vision AutoML with the existing dataset.