Amazon SageMaker Ground Truth Adds Built-in Workflows for the Verification and Adjustment of Data Labels
Today, Amazon SageMaker Ground Truth launched built-in workflows for label verification and label adjustment. One way that ML practitioners have improved the accuracy of their labeled data is through using audit workflows. Audit workflows enable a group of reviewers to verify the accuracy of labels (a process called label verification) or adjust them (a process called label adjustment) if needed.
Today, Amazon SageMaker Ground Truth launched built-in workflows for label verification and label adjustment. One way that ML practitioners have improved the accuracy of their labeled data is through using audit workflows. Audit workflows enable a group of reviewers to verify the accuracy of labels (a process called label verification) or adjust them (a process called label adjustment) if needed.
To use these new built-in workflows, you can chain an existing Amazon SageMaker Ground Truth labeling job into a verification or adjustment job, or you can easily import your existing labels for a verification or adjustment job. These features are supported for bounding boxes and semantic segmentation labels.
Learn more about this feature from our launch blog and documentation.