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.
@article{arxiv.1310.5426,
title = {MLI: An API for Distributed Machine Learning},
author = {Evan R. Sparks and Ameet Talwalkar and Virginia Smith and Jey Kottalam and Xinghao Pan and Joseph Gonzalez and Michael J. Franklin and Michael I. Jordan and Tim Kraska},
journal= {arXiv preprint arXiv:1310.5426},
year = {2013}
}