Systems for ML

Workshop on Systems for ML at NeurIPS 2019

December 13, 2019

Workshop on Systems for ML

A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. The goal of this workshop is to bring together experts working at the crossroads of machine learning, system design and software engineering to explore the challenges faced when building practical large-scale ML systems. In particular, we aim to elicit new connections among these diverse fields, and identify tools, best practices and design principles. We also want to think about how to do research in this area and properly evaluate it. The workshop will cover ML and AI platforms and algorithm toolkits, as well as dive into machine learning-focused developments in distributed learning platforms, programming languages, data structures, GPU processing, and other topics.

This workshop will follow the successful model we have previously run with the ML Systems Workshops @ NeurIPS and ICML, and SOSP

Workshop Recordings

This year, NeurIPS recorded the workshop. Links to the recordings of individual sessions can be found on the schedule.

Mailing List

Join our low-volume mailing list for announcements about future workshops and other events of interest to the Systems for ML community:

Call for Papers

We invite participation in the Systems for ML Workshop which will be held in conjunction with NeurIPS 2019 in Vancouver, Canada.

Organizing Committee

Contact us:

Program Committee

  • François Belletti, Google AI
  • Sarah Bird, Microsoft
  • Vladimir Feinberg, Sisu Data
  • Garth Gibson, Vector Institute and Carnegie Mellon University
  • Joseph E. Gonzalez, UC Berkeley
  • Asim Kadav, NEC labs
  • Aparna Lakshmi Ratan, Facebook
  • Mathias Lécuyer, Microsoft Research
  • Jinyang Li, NYU
  • Daniel Lo, Microsoft
  • Mihir Nanavati, Microsoft Research
  • Aurojit Panda, NYU
  • Christopher Ré, Stanford University
  • Siddhartha Sen, Microsoft Research
  • Riley Spahn, Columbia University
  • Andrew Tulloch, Facebook
  • Haoyu Zhang, Google AI