Machine Learning Pipeline for Earth Science Using Sagemaker

Abstract

Machine learning (ML) is gaining popularity in the Earth science domain. Higher the amount of quality data, the better the model. CPU training of such ML models is slow; GPU is used for training. Maintaining GPU servers is an additional responsibility. Multiple iterations of experiments needed before a better performing model is trained. Dataset creation, versioning of datasets, models, and experiments is hard.