Prepare and Label Data - Amazon SageMaker

Prepare and Label Data

To analyze your data and evaluate machine learning models on Amazon SageMaker, use Amazon SageMaker Processing. With Processing, you can use a simplified, managed experience on Amazon SageMaker to run your data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation. You can also use the Amazon SageMaker Processing APIs during the experimentation phase and after the code is deployed in production to evaluate performance.

To train a machine learning model, you need a large, high-quality, labeled dataset. You can label your data using Amazon SageMaker Ground Truth. Choose from one of the Ground Truth built-in task types or create your own custom labeling workflow. To improve the accuracy of your data labels and reduce the total cost of labeling your data, use Ground Truth enhanced data labeling features like automated data labeling and annotation consolidation.