Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA
Joseph Bradley

Joseph Bradley
Software Engineer, Databricks

@jkbatcmu

Joseph Bradley is a software engineer working on machine learning at Databricks. Joseph is an Apache Spark committer and PMC member. Previously, he was a postdoc at UC Berkeley. Joseph holds a PhD in machine learning from Carnegie Mellon University, where he focused on scalable learning for probabilistic graphical models, examining trade-offs between computation, statistical efficiency, and parallelization.

Sessions

11:50am12:30pm Thursday, March 16, 2017
Spark & beyond
Location: 210 A/E
Secondary topics:  Deep learning, Hardcore Data Science
Joseph Bradley (Databricks), Tim Hunter (Databricks, Inc.)
Average rating: ***..
(3.75, 4 ratings)
Joseph Bradley and Tim Hunter share best practices for building deep learning pipelines with Apache Spark, covering cluster setup, data ingest, tuning clusters, and monitoring jobs—all demonstrated using Google’s TensorFlow library. Read more.
1:50pm2:30pm Thursday, March 16, 2017
Location: Table A
Joseph Bradley (Databricks)
Joseph’s office hour is an excellent opportunity to discuss the best practices for building deep learning pipelines with Apache Spark, as well as any other topics about machine learning and graph processing on Spark you'd like to talk about (e.g., MLlib's current activity and roadmap, GraphFrames, and Databricks). Read more.