MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Abstract
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.
BibTeX
@article{DBLP:journals/corr/abs-1904-03257,
author = {Alexander Ratner and
Dan Alistarh and
Gustavo Alonso and
David G. Andersen and
Peter Bailis and
Sarah Bird and
Nicholas Carlini and
Bryan Catanzaro and
Eric Chung and
Bill Dally and
Jeff Dean and
Inderjit S. Dhillon and
Alexandros G. Dimakis and
Pradeep Dubey and
Charles Elkan and
Grigori Fursin and
Gregory R. Ganger and
Lise Getoor and
Phillip B. Gibbons and
Garth A. Gibson and
Joseph E. Gonzalez and
Justin Gottschlich and
Song Han and
Kim M. Hazelwood and
Furong Huang and
Martin Jaggi and
Kevin G. Jamieson and
Michael I. Jordan and
Gauri Joshi and
Rania Khalaf and
Jason Knight and
Jakub Konecn{\'{y}} and
Tim Kraska and
Arun Kumar and
Anastasios Kyrillidis and
Jing Li and
Samuel Madden and
H. Brendan McMahan and
Erik Meijer and
Ioannis Mitliagkas and
Rajat Monga and
Derek Gordon Murray and
Dimitris S. Papailiopoulos and
Gennady Pekhimenko and
Theodoros Rekatsinas and
Afshin Rostamizadeh and
Christopher R{\'{e}} and
Christopher De Sa and
Hanie Sedghi and
Siddhartha Sen and
Virginia Smith and
Alex Smola and
Dawn Song and
Evan R. Sparks and
Ion Stoica and
Vivienne Sze and
Madeleine Udell and
Joaquin Vanschoren and
Shivaram Venkataraman and
Rashmi Vinayak and
Markus Weimer and
Andrew Gordon Wilson and
Eric P. Xing and
Matei Zaharia and
Ce Zhang and
Ameet Talwalkar},
title = {SysML: The New Frontier of Machine Learning Systems},
journal = {CoRR},
volume = {abs/1904.03257},
year = {2019},
url = {http://arxiv.org/abs/1904.03257},
archivePrefix = {arXiv},
eprint = {1904.03257},
timestamp = {Thu, 02 May 2019 12:28:04 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1904-03257},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
From DBLP