Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Satish Varma Dandu

Satish Varma Dandu
Software Engineering Manager, NVIDIA

@dsvarma

Satish Varma Dandu is a data science and engineering manager at NVIDIA, where he leads teams that build massive end-to-end big data and deep learning platforms, handling billions of events per day for real-time analytics, data warehousing, and AI platforms using deep learning to improve the user experience for millions of users. Previously, Satish led data engineering teams at startups and large public companies. His areas of interest are in building large-scale engineering platforms, big data engineering, GPU data acceleration, and deep learning. Satish holds an MS in computer science from the University of Houston and is currently enrolled in the management program at Stanford University.

Sessions

2:55pm3:35pm Wednesday, September 27, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Deep learning
Joshua Patterson (NVIDIA), Michael Balint (NVIDIA), Satish Varma Dandu (NVIDIA)
Average rating: ****.
(4.00, 1 rating)
How can deep learning be employed to create a system that monitors network traffic, operations data, and system logs to reliably flag risk and unearth potential threats? Satish Dandu, Joshua Patterson, and Michael Balint explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools. Read more.