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Scaled Machine Learning Workshop

Machine Learning is evolving to utilize new hardware such as GPUs and large commodity clusters. University and industry researchers have been using these new computing platforms to scale machine learning across many dimensions.

This conference aims to bring together researchers running machine learning algorithms on a variety of computing platforms to foster discussions between them. The goal is to encourage algorithm designers for these platforms to help each other scale and transplant ideas between the platforms.

Speakers and Panelists

Jeff Dean (Google)
Scaled Machine Learning with TensorFlow and XLA
Ion Stoica (UC Berkeley and Databricks)
Distributed Machine Learning and the Berkeley RISE lab
Reza Zadeh (Stanford and Matroid)
Scaling Computer Vision at Matroid
Rajat Monga (Google)
Panel on Scaled ML
Ben Lorica (O'Reilly)
Panel on Scaled ML
Wes McKinney (Two Sigma)
Scaling Challenges in Pandas 2.0
David Ku (Microsoft)
Scaled Machine Learning at Microsoft
Ian Buck (NVIDIA)
Scaled Machine Learning on NVIDIA GPUs
Claudia Perlich (Dstillery)
Andy Feng (Yahoo)
TensorFlow on Apache Spark
DB Tsai (Netflix)
Panel on Scaled ML
Ziya Ma (Intel)
Scaling ML on Intel CPUs
Matei Zaharia (Stanford)
DAWN: Infrastructure for usable Machine Learning
Ilya Sutskever (OpenAI)
Scaling Reinforcement Learning


Saturday March 25th 2017

  • 08:45-09:00 Reza Zadeh, Introduction
  • 09:00-10:00 Ion Stoica
  • 10:00-11:00 Reza Zadeh
  • 11:00-11:30 David Ku
  • 11:30-12:00 Matei Zaharia
  • 12:00-13:00 Lunch Break
  • 13:00-14:00 Jeff Dean
  • 14:00-15:00 Panel: Ziya Ma, Rajat Monga, DB Tsai, Ben Lorica
  • 15:00-15:30 Claudia Perlich
  • 15:30-16:00 Break
  • 16:00-16:30 Ilya Sutskever
  • 16:30-17:00 Wes Mckinney
  • 17:00-17:30 Ian Buck
  • 17:30-18:00 Andy Feng


Please register here:

Saturday, March 25, 2017 -
8:30am to 6:00pm