Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Dan Crankshaw

Dan Crankshaw
Graduate Student, UC Berkeley RISELab

Dan Crankshaw is a PhD student in the CS Department at UC Berkeley, where he works in the RISELab. After cutting his teeth doing large-scale data analysis on cosmology simulation data and building systems for distributed graph analysis, Dan has turned his attention to machine learning systems. His current research interests include systems and techniques for serving and deploying machine learning, with a particular emphasis on low-latency and interactive applications.

Sessions

11:50am–12:30pm Wednesday, March 7, 2018
Deploying and monitoring interactive machine learning applications with Clipper
Data science and machine learning, Streaming systems and real-time applications
 Location: LL20 A
Dan Crankshaw (UC Berkeley RISELab)
Average rating: ****.
(4.25, 4 ratings)
Clipper is an open source, general-purpose model-serving system that provides low-latency predictions under heavy serving workloads for interactive applications. Dan Crankshaw offers an overview of the Clipper serving system and explains how to use it to serve Apache Spark and TensorFlow models on Kubernetes. Read more.