Creating high-quality training sets for machine learning models
summary
SageMaker Ground Truth adds synthetic-data generation to produce labeled computer vision training sets using a library of digital assets and scene controllers, delivering 1 million assets to bootstrap projects, automated scene randomization, and fidelity/diversity reports that compare metrics like brightness and object-size distributions. The service exports data to S3, produces tens of thousands of labeled photorealistic images for customers, and powers real deployments—enabling over 1 million picks per day for package-handling robots and replicating rare defects for food-inspection models.
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chapters (12)
- 0:00Intro
- 0:45Typical machine learning process
- 1:42Data labeling challenges
- 2:16SageMaker Ground Truth capabilities
- 4:27SageMaker training data capabilities
- 5:20SageMaker Ground Truth support for synthetic data
- 7:27Scene and object
- 9:29Highly accurate ground truth across multiple modalities
- 12:14Get started with SageMaker Ground Truth synthetic data
- 14:01SageMaker Ground Truth synthetic data process workflow
- 17:14Synthetic image fidelity and diversity report
- 18:18Synthetic data generation use cases
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