What is Validation set adjustment while the system is auto labeling

When an auto labeling job is initiated by Ground Truth, a random sample of input data is selected and sent to Human workers for labeling. Upon the return of this data, a training set and a validation set are created. Ground Truth uses these datasets to train and validate the model used for auto labeling.

Much like with ML models, cross-validation is done by using a complementary subset of the data from the input data to evaluate the model. In Ground Truth auto labeling, this Validation set of data is periodically adjusted ( at every iteration of the labeling job) to improve the accuracy of the automated labels.

If you have further specific questions around your workflows or require a deep dive on your logs in this regard, you may open a support case using this link , as we may require details that are non-public information, and we will be happy to assist you further.

How it works - https://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html#sms-automated-labeling-how-it-works

Cross-Validation - https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html