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