Highly accurate training datasets built by machine learning that saves up to 70% of labeling costs
AWS SageMaker Ground Truth is a resource for building high-quality training datasets for machine learning. You will be able to provide human and public labelers with built-in workflows and interfaces for common labeling tasks. Data is eventually labeled automatically, so you can save up to 70% of data labeling costs because Ground Truth learns from the data provided from human labelers.
Large volumes of high-quality data give Ground Truth the ability to build machine learning datasets successfully. The problems with manually labeled data (high costs, long label times, and complicated labeling) are solved with Ground Truth labeling loop. Labeling data is time consuming because of all the parameters involved in an environment. In Ground Truth, humans label the data with a high-confidence grade, then the model learns to make correct automatic labeling decisions based off the provided labels.
The large volumes of data in Ground Truth are processed quickly because the resource can learn from the human labeled data, and get progressively better over time at making automatic labels.
There are many benefits to using AWS Ground Truth. You can reduce data labeling costs by up to 70% because the automatic labeled data gets smarter and smarter with every label provided by the human labelers. You can also work with public and private human labelers depending on the nature and sensitivity of your data. Achieve accurately labeled data results