Scaled Machine Learning

Stanford University
March 25th 2017, 8:30am - 6:00pm

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)
    The future of TensorFlow
  • 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
  • A tutorial on TensorFlow




To be released after event.


Please register here.

Directions and Parking

The meeting is in CEMEX Auditorium, on Stanford University campus. The exact address is:

CEMEX Auditorium
655 Knight Way
Stanford, CA 94305

Previous Years

Scaled ML 2016

Media Partner

Contact Organizers

Register Here