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arXiv · 1310.5426

MLI: An API for Distributed Machine Learning

Evan R. Sparks·Ameet Talwalkar·Virginia Smith·Jey Kottalam·Xinghao Pan·Joseph Gonzalez·Michael J. Franklin·Michael I. Jordan·Tim Kraska
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Abstract

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.

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Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska. 2013-10-25. MLI: An API for Distributed Machine Learning. https://arxiv.org/abs/1310.5426

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