Put AI to Work
April 15-18, 2019
New York, NY

Building reinforcement learning models and AI applications with Ray

Robert Nishihara (University of California, Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (University of California, Berkeley), Eric Liang (University of California, Berkeley, RISELab)
1:45pm5:15pm Tuesday, April 16, 2019
Location: Petit Trianon
Secondary topics:  Deep Learning and Machine Learning tools, Reinforcement Learning
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Machine learning researchers and practitioners and data scientists

Prerequisite knowledge

  • Familiarity with Python programming, basic machine learning concepts, and reinforcement learning

Materials or downloads needed in advance

  • A laptop

What you'll learn

  • Learn how to develop simple RL applications at scale with Ray and manage Ray on clusters

Description

Building end-to-end AI applications is challenging, and building the next generation of AI applications, such as online learning and reinforcement learning (RL) is even more challenging. That’s because these applications exhibit a large variety of computational patterns (e.g., data processing, simulations, model training, model serving), and none of the existing frameworks can efficiently support all these patterns at scale.

Join Robert Nishihara, Philipp Moritz, Ion Stoica, and Eric Liang to learn how Ray seamlessly and efficiently supports these computational patterns and hence provides an ideal platform for building AI applications. Through lecture and hands-on exercises, you’ll take a deep dive into Ray, learn its API, and implement several state-of-the-art AI applications, including an end-to-end application that involves training an RL model and serving predictions from it.

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.

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 Ion Stoica

Ion Stoica

University of California, Berkeley

Ion Stoica is a professor in the electrical engineering and computer sciences (EECS) department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Previously, he worked on dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He’s the cofounder of Databricks—a startup to commercialize Apache Spark—and Conviva—a startup to commercialize technologies for large-scale video distribution. Ion is an ACM fellow and has received numerous awards, including inclusion in the SIGOPS Hall of Fame (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001).

Photo of Eric Liang

Eric Liang

University of California, Berkeley, RISELab

Eric Liang is a second-year PhD student working in the University of California, Berkeley, RISELab with Ion Stoica. He works on frameworks and applications for machine learning and reinforcement learning. Previously, he spent several years working on systems in industry at Databricks and Google.