Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: 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

2:40pm–3:20pm Wednesday, March 7, 2018
Best practices for productionizing Apache Spark MLlib models
Big data and data science in the cloud, Data science and machine learning
 Location: LL20 D
Joseph Bradley (Databricks)
Average rating: *****
(5.00, 1 rating)
Joseph Bradley discusses common paths to productionizing Apache Spark MLlib models and shares engineering challenges and corresponding best practices. Along the way, Joseph covers several deployment scenarios, including batch scoring, Structured Streaming, and real-time low-latency serving. Read more.