Dean Wampler

Head of Evangelism, Anyscale

Dean Wampler (@deanwampler) is an expert in streaming systems, focusing on ML/AI. He is Head of Evangelism at, which is developing Ray for distributed Python. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for streaming data applications with popular open source tools. Dean has written books for O’Reilly and contributed to several open source projects. He is a frequent conference speaker and tutorial teacher, and a co-organizer of several conferences and user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington.

Past sessions

Summit 2020 Ray: Enterprise-Grade, Distributed Python

June 25, 2020 05:00 PM PT

Ray ( is an open-source, distributed framework from U.C. Berkeley's RISELab that easily scales Python applications from a laptop to a cluster. It was developed to solve the general challenges of reinforcement learning, but it is flexible for any demanding workload that requires the following:

  1. Low-latency scheduling and execution of small-to-large 'tasks' that perform a wide variety of computation chores, with logical sequencing of dependent tasks.
  2. Management of 'arbitrary', distributed state, with thread-safe updates and access from other Ray tasks across a cluster.
  3. Near-linear scaling.
  4. An intuitive API that hides complexity from the user.

Ray has been used for reinforcement learning, hyper parameter tuning, model serving, and other applications in clusters up to thousands of nodes. I'll discuss examples that illustrate how Ray can be used with Spark to build robust, scalable data applications for enterprises, when to use Ray versus alternative choices, and how to adopt it in your projects.

Summit 2014 Databricks Application Spotlight

June 30, 2014 05:00 PM PT

The Application Spotlight will highlight selected “Certified on Spark” applications that leverage Spark to help their users derive greater value from their data. For each application their will be a brief demo of key functionality followed by a fireside chat discussing the developers experience with Spark, lessons learned, and wish list for the future.