Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Ray: A distributed execution framework for emerging AI applications

Philipp Moritz (University of California, Berkeley), Robert Nishihara (University of California, Berkeley)
2:35pm3:15pm Wednesday, June 28, 2017
Implementing AI
Location: Murray Hill E/W Level: Intermediate
Secondary topics:  Machine Learning
Average rating: *****
(5.00, 2 ratings)

Prerequisite Knowledge

  • A general understanding of Python, machine learning, and distributed computation

What you'll learn

  • Understand how to build AI application at scale using Ray


AI applications are increasingly dynamic and interactive and work in real time. These applications are being deployed not only to serve predictions using static models but also as tightly integrated components of feedback loops. Philipp Moritz and Robert Nishihara discuss the challenges that these applications pose for distributed systems and offer an overview of Ray, a new system designed to support these emerging applications.

Photo of Philipp Moritz

Philipp Moritz

University of California, Berkeley

Philipp Moritz is a PhD candidate in the Electrical Engineering and Computer Sciences (EECS) Department at the University of California, Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He’s a member of the Statistical AI Lab and the RISELab.

Photo of Robert Nishihara

Robert Nishihara

University of California, Berkeley

Robert Nishihara is a fourth-year PhD student working in the University of California, Berkeley, RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.