why SageMaker Ground Truth automated labelling?

Automated data labeling is labeling of data using machine learning. Amazon SageMaker Ground Truth will first select a random sample of data and send it to humans to be labeled. The results are then used to train a labeling model that attempts to label a new sample of raw data automatically. The labels are committed when the model can label the data with a confidence score that meets or exceeds a high threshold. Where the confidence score falls below this threshold, the data is sent to human labelers. Some of the data labeled by humans is used to generate a new training dataset for the labeling model, and the model is automatically retrained to improve its accuracy. This process repeats with each sample of raw data to be labeled. The labeling model becomes more capable of automatically labeling raw data with each iteration, and less data is routed to humans.