Amazon SageMaker Ground Truth
Apply human feedback across the ML lifecycle to create high-quality models
Get human generated data to customize models on specific tasks or with company and industry specific data
Create high quality training datasets to improve model accuracy with an expert, on-demand workforce
Accelerate and automate human-in-the-loop tasks, from data generation and annotation to model review and customization, while reducing costs
Amazon SageMaker Ground Truth offers the most comprehensive set of human-in-the-loop capabilities, allowing you to harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. You can complete a variety of human-in-the-loop tasks with SageMaker Ground Truth, from data generation and annotation to model review and customization, either through a self-service or an AWS-managed offering.
How it works
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AWS-Managed
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Self-Serve
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AWS-Managed
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Self-Serve
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Use cases
Get started with key use cases quickly
Example and Demonstration Data
Use human generated data such as text summarizations, Q&A pairs, citations, and captions to train FMs for AI-powered applications
Comparison and Ranking Data
Use human feedback to rank and/or classify model responses (e.g. from best to worst), and use this data to train FMs
Data Labeling
Label text, images, video, audio, and point cloud to train ML models for a range of use cases
Customers
How to get started
Get started with data labeling
Set up your own labeling workflow with SageMaker Ground Truth.
Learn more about SageMaker Ground Truth
Access additional resources, documentation and learning materials.