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Elastic machine learning algorithms in Amazon SageMaker

By Edo Liberty, Zohar Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang (Bernie) Wang, Jan Gasthaus, Lorenzo Stella, Syama Rangapuram, David Salinas, Sebastian Schelter, Alex Smola
2020
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There is a large body of research on scalable machine learning (ML). Nevertheless, training ML models on large, continuously evolving datasets is still a difficult and costly under-taking for many companies and institutions. We discuss such challenges and derive requirements for an industrial-scale ML platform. Next, we describe the computational model behind Amazon SageMaker which is designed to meet such challenges. SageMaker is an ML platform provided as part of Amazon Web Services (AWS), and supports incremental training, resumable and elastic learning as well as automatic hyperparameter optimization. We detail how to adapt several popular ML algorithms to its computational model. Finally, we present an experimental evaluation on large datasets, comparing SageMaker to several scalable, JVM-based implementations of ML algorithms, which we significantly outperform with regard to computation time and cost.
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