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

Sergey Ermolin
Solutions Architect and TPM, Deep Learning, Spark Analytics, and Big Data Technologies, Intel

Website

Sergey Ermolin is a software solutions architect for deep learning, Spark analytics, and big data technologies at Intel. A Silicon Valley veteran with a passion for machine learning and artificial intelligence, Sergey has been interested in neural networks since 1996, when he used them to predict aging behavior of quartz crystals and cesium atomic clocks made by Hewlett-Packard. Sergey holds an MSEE and a certificate in mining massive datasets from Stanford and BS degrees in both physics and mechanical engineering from California State University, Sacramento.

Sessions

11:50am–12:30pm Wednesday, March 7, 2018
Accelerating deep learning on Apache Spark using BigDL with coarse-grained scheduling
Big data and data science in the cloud, Data science and machine learning
 Location: LL21 B
Sergey Ermolin (Intel), Shivaram Venkataraman (Microsoft Research)
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The BigDL framework scales deep learning for large datasets using Apache Spark. However there is significant scheduling overhead from Spark when running BigDL at large scale. Shivaram Venkataraman and Sergey Ermolin outline a new parameter manager implementation that along with coarse-grained scheduling can provide significant speedups for deep learning models like Inception and VGG. Read more.
5:10pm–5:50pm Wednesday, March 7, 2018
Improving user-merchant propensity modeling using neural collaborative filtering and wide and deep models on Spark BigDL at scale
Big data and data science in the cloud, Data science and machine learning
 Location: LL21 B
Sergey Ermolin (Intel), Suqiang Song (Mastercard)
Sergey Ermolin and Suqiang Song demonstrate how to use Spark BigDL wide and deep and neural collaborative filtering (NCF) algorithms to predict a user’s probability of shopping at a particular offer merchant during a campaign period. Along the way, they compare the deep learning results with those obtained by MLlib’s alternating least squares (ALS) approach. Read more.