MLSys: The New Frontier of Machine Learning Systems

MLSys: The New Frontier of Machine Learning Systems. Ratner, A., Alistarh, D., Alonso, G., Andersen, D. G., Bailis, P., Bird, S., Carlini, N., Catanzaro, B., Chayes, J., Chung, E., Dally, B., Dean, J., Dhillon, I. S., Dimakis, A., Dubey, P., Elkan, C., Fursin, G., Ganger, G. R., Getoor, L., Gibbons, P. B., Gibson, G. A., Gonzalez, J. E., Gottschlich, J., Han, S., Hazelwood, K., Huang, F., Jaggi, M., Jamieson, K., Jordan, M. I., Joshi, G., Khalaf, R., Knight, J., Konečný, J., Kraska, T., Kumar, A., Kyrillidis, A., Lakshmiratan, A., Li, J., Madden, S., McMahan, H. B., Meijer, E., Mitliagkas, I., Monga, R., Murray, D., Olukotun, K., Papailiopoulos, D., Pekhimenko, G., Rekatsinas, T., Rostamizadeh, A., Ré, C., Sa, C. D., Sedghi, H., Sen, S., Smith, V., Smola, A., Song, D., Sparks, E., Stoica, I., Sze, V., Udell, M., Vanschoren, J., Venkataraman, S., Vinayak, R., Weimer, M., Wilson, A. G., Xing, E., Zaharia, M., Zhang, C., & Talwalkar, A. 2019.

Paper abstract bibtex

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.

@misc{ratner2019mlsys,
    title={{MLSys: The New Frontier of Machine Learning Systems}},
    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 Jennifer Chayes and Eric Chung and Bill Dally and Jeff Dean and Inderjit S. Dhillon and Alexandros 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 Hazelwood and Furong Huang and Martin Jaggi and Kevin Jamieson and Michael I. Jordan and Gauri Joshi and Rania Khalaf and Jason Knight and Jakub Konečný and Tim Kraska and Arun Kumar and Anastasios Kyrillidis and Aparna Lakshmiratan and Jing Li and Samuel Madden and H. Brendan McMahan and Erik Meijer and Ioannis Mitliagkas and Rajat Monga and Derek Murray and Kunle Olukotun and Dimitris Papailiopoulos and Gennady Pekhimenko and Theodoros Rekatsinas and Afshin Rostamizadeh and Christopher Ré and Christopher De Sa and Hanie Sedghi and Siddhartha Sen and Virginia Smith and Alex Smola and Dawn Song and Evan 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 Xing and Matei Zaharia and Ce Zhang and 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.},
    year={2019},
    date={2019-03-29},
    eprint={1904.03257},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    pubstate={preprint},
    url={http://arxiv.org/abs/1904.03257},
	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.},
    keywords={whitepaper}
}


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