MLA-C01 · Question #162
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frame
The correct answer is D. Use the labeling interface of Amazon Augmented AI (Amazon A2I) with Amazon Rekognition to. Using Amazon Rekognition to automatically detect and label motion-related features in videos, combined with Amazon Augmented AI for human review only when needed, significantly reduces manual labeling effort and time while integrating well with an existing SageMaker retraining
Question
A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames. The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time. Which solution will meet these requirements?
Options
- AUse SageMaker Ground Truth to annotate the video frames.
- BUse SageMaker JumpStart to use pre-trained computer vision models to develop a labeling
- CUse SageMaker Data Wrangler to create a data workflow. Use the workflow to optimize the
- DUse the labeling interface of Amazon Augmented AI (Amazon A2I) with Amazon Rekognition to
How the community answered
(60 responses)- A8% (5)
- B3% (2)
- C15% (9)
- D73% (44)
Explanation
Using Amazon Rekognition to automatically detect and label motion-related features in videos, combined with Amazon Augmented AI for human review only when needed, significantly reduces manual labeling effort and time while integrating well with an existing SageMaker retraining
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