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

Arvind Surve
Machine Learning Engineer and Software Architect , IBM

Website

Arvind Surve is Data Scientist, Architect in AI & ML, IBM Analytics group. Arvind is a SystemML contributor and committer. He has worked for IBM for 19+ years. Arvind has presented at the 2015 Data Engineering Conference in Tokyo,
Strata Data 2017 Conference, San Jose, and to the Chicago Spark User group, Chicago. He holds an MS in digital electronics and communication systems and an MBA in finance and marketing.

Sessions

4:20pm5:00pm Thursday, March 16, 2017
Data science & advanced analytics
Location: 230 C Level: Advanced
Secondary topics:  Hardcore Data Science
Many iterative machine-learning algorithms can only operate efficiently when a large matrix of training data fits in the main memory. Frederick Reiss and Arvind Surve offer an overview of compressed linear algebra, a technique for compressing training data and performing key operations in the compressed domain that lets you build models over big data with small machines. Read more.