A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations. Which solution will meet these requirements?
A.
Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
B.
Data augmentation by using an Amazon Bedrock knowledge base
C.
Image recognition by using Amazon Rekognition
D.
Data summarization by using Amazon QuickSight Q
Human-in-the-loop validation combines the efficiency of machine learning with human expertise to ensure high-quality labeled data. Amazon SageMaker Ground Truth Plus enables you to have human labelers validate and correct model predictions, which reduces errors in annotations and increases the accuracy of the training data. This is particularly useful when you need to generate accurate images or annotations for protective eyewear and want to ensure that the annotations are reliable.
A: Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
Explanation:
Amazon SageMaker Ground Truth Plus is designed for creating high-quality labeled datasets with human-in-the-loop validation to ensure accuracy. This solution helps minimize the risk of incorrect annotations by involving human reviewers to verify and correct the model's predictions. It is particularly useful for scenarios requiring precision, such as generating images with specific requirements like protective eyewear.
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