SysML: The New Frontier of Machine Learning Systems

SysML: The New Frontier of Machine Learning Systems
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SysML：机器学习系统的新领域

DOI：
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发表时间：
2019
期刊：
arXiv.org
影响因子：
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通讯作者：
Ameet Talwalkar
Ameet Talwalkar
中科院分区：
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文献类型：
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作者：
Alexander J. Ratner;Dan Alistarh;G. Alonso;D. Andersen;Peter D. Bailis;Sarah Bird;Nicholas Carlini;Bryan Catanzaro;E. Chung;B. Dally;J. Dean;I. Dhillon;A. Dimakis;P. Dubey;C. Elkan;G. Fursin;G. Ganger;L. Getoor;Phillip B. Gibbons;Garth A. Gibson;Joseph E. Gonzalez;Justin Emile Gottschlich;Song Han;K. Hazelwood;Furong Huang;Martin Jaggi;Kevin G. Jamieson;Michael I. Jordan;Gauri Joshi;Rania Y. Khalaf;J. Knight;Jakub Konecný;Tim Kraska;Arun Kumar;Anastasios Kyrillidis;Jing Li;S. Madden;H. B. McMahan;E. Meijer;Ioannis Mitliagkas;R. Monga;D. Murray;Dimitris Papailiopoulos;Gennady Pekhimenko;Theodoros Rekatsinas;Afshin Rostamizadeh;C. Ré;Christopher De Sa;Hanie Sedghi;S. Sen;Virginia Smith;Alex Smola;D. Song;Evan R. Sparks;I. Stoica;V. Sze;Madeleine Udell;J. Vanschoren;S. Venkataraman;R. Vinayak;Markus Weimer;A. Wilson;E. Xing;M. Zaharia;Ce Zhang;Ameet Talwalkar

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机器学习（ML）技术正在快速增长。然而，设计和实现在现实世界部署中支持ML模型的系统仍然是一个重大障碍，这在很大程度上是由于现代ML方法的开发和部署配置文件完全不同，以及更广泛采用带来的实际问题。我们建议在传统系统和机器学习社区的交叉点培育一个新的系统机器学习研究社区，重点关注机器学习的硬件系统，机器学习的软件系统，以及为预测准确性之外的指标优化的机器学习。为了做到这一点，我们描述了一个新的会议，SysML，明确针对系统和机器学习的交叉点的研究，计划委员会在系统和ML专家之间平均分配，并明确关注两者交叉点的主题。
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, SysML, 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.