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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Schedule: Deep Learning sponsored by NVIDIA sessions

11:20am–12:00pm Wednesday, 09/12/2018
Location: 1 E15
Ward Eldred (NVIDIA)
Average rating: *****
(5.00, 2 ratings)
Ward Eldred offers an overview of the types of analytical problems that can be solved using deep learning and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Read more.
1:15pm–1:55pm Wednesday, 09/12/2018
Location: 1 E15
Darrin Johnson (NVIDIA)
While every enterprise is on a mission to infuse its business with deep learning, few know how to build the infrastructure to get them there. Darrin Johnson shares insights and best practices learned from NVIDIA's deep learning deployments around the globe that you can leverage to shorten deployment timeframes, improve developer productivity, and streamline operations. Read more.
2:05pm–2:45pm Wednesday, 09/12/2018
Location: 1 E15
Michael Balint (NVIDIA)
Michael Balint explains how NVIDIA employs its own distribution of Kubernetes, in conjunction with DGX hardware, to make the most efficient use of GPU resources and scale its efforts across a cluster, allowing multiple users to run experiments and push their finished work to production. Read more.
4:35pm–5:15pm Wednesday, 09/12/2018
Location: 1 E15
Alen Capalik (, Jim McHugh (NVIDIA), SriSatish Ambati (, Tim Delisle (Datalogue)
Explore case studies from Datalogue,, and that demonstrate how GPU-accelerated analytics, machine learning, and ETL help companies overcome slow queries and tedious data preparation process, dynamically correlate among data, and enjoy automatic feature engineering. Read more.
5:25pm–6:05pm Wednesday, 09/12/2018
Location: 1 E15
Renee Yao (NVIDIA)
Average rating: *****
(5.00, 1 rating)
Renee Yao explains how generative adversarial networks (GAN) are successfully used to improve data generation and explores specific real-world examples where customers have deployed GANs to solve challenges in healthcare, space, transportation, and retail industries. Read more.