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 is 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.
In this tutorial, we will illustrate how Ray can seamlessly and efficiently support these computational patterns, and hence provides an ideal platform for building AI applications. This tutorial will be hands on, and participants will take a deep dive into Ray, learn its API, and implement several state-of-the-art AI applications including an end-to-end application which involves training an RL model and serving predictions from it.
Robert Nishihara is a fourth-year PhD student working in the UC Berkeley RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.
Philipp Moritz is a PhD candidate in EECS at UC Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He is a member of the Statistical AI Lab and the RISELab.
Ion Stoica is a professor in the EECS Department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Ion’s previous work includes dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He is 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).
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