- 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. 2019. SysML: The New Frontier of Machine Learning Systems. 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, 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.
