Apply human feedback across the ML lifecycle to create or evaluate high-quality models

Why SageMaker Ground Truth?

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, customization, and evaluation, either through a self-service or an AWS-managed offering.

How it works

Create high-quality training datasets without having to build labeling applications or manage a labeling workforce.

Benefits of SageMaker Ground Truth

Get human generated data

Get human generated data to customize models on specific tasks or with company and industry specific data

Evaluate models

Use human evaluation to compare and select the foundation model (FM) that is best suited for your use case

Create high quality datasets

Create high quality training datasets to improve model accuracy with an expert, on-demand workforce

Accelerate human-in-the-loop tasks

Accelerate and automate human-in-the-loop tasks, from data generation and annotation to model review, customization, and evaluation, while reducing costs

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

Evaluating and Red Teaming

Enable humans to easily review, compare, and evaluate model outputs to discover vulnerabilities, reduce bias, and eliminate toxicity

Data Labeling

Label text, images, video, audio, and point cloud to train ML models for a range of use cases