BigDL: Bringing Ease of Use of Deep Learning for Apache Spark - Databricks

BigDL: Bringing Ease of Use of Deep Learning for Apache Spark

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BigDL is a distributed deep learning framework for Apache Spark open sourced by Intel. BigDL helps make deep learning more accessible to the Big Data community, by allowing them to continue the use of familiar tools and infrastructure to build deep learning applications. With BigDL, users can write their deep learning applications as standard Spark programs, which can then directly run on top of existing Spark or Hadoop clusters. In this session, we will introduce BigDL, how our customers use BigDL to build End to End ML/DL applications, platforms on which BigDL is deployed and also provide an update on the latest improvements in BigDL v0.1, and talk about further developments and new upcoming features of BigDL v0.2 release (e.g., support for TensorFlow models, 3D convolutions, etc.).

About Radhika Rangarajan

Radhika is the Director of Engineering for Big Data Solutions within Intel’s Software and Services Group, where she manages several open source projects and partner engagements, specifically on Apache Spark and machine learning. She leads the team responsible for Driving Enterprise, Cloud and Customer partnerships for Spark Analytics and Deep learning with BigDL. Radhika is also the Co-founder and the Director for the West Coast chapter of Women in Big Data, a grassroots community focused on strengthening the diversity and championing the success of Women in Big Data and Analytics.

About Jason Dai

Jason is currently a Sr. Principle Engineer and Chief Architect of Big Data Technologies at Intel, leading the development of advanced Big Data analytics (incl. distributed machine learning and deep learning). He is an internationally recognized expert on big data, cloud and distributed machine learning; he is the co-chair of Strata Data Conference Beijing, a committer and PMC member of Apache Spark project, and the chief architect of BigDL project (, a distributed deep learning framework on Apache Spark.