AI & Deep Learning
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Date: July 15, 2020
Time: 8:00am – 9:00am PT
Duration: 1 hour

We'll bring together AI implementers who have deployed AI at scale using NVIDIA DGX systems. There will be a focus on specific technical challenges, solution design considerations, and the best practices that implementers learned from their respective solutions.

By attending this webinar you'll learn:
  • How to set up your AI projects for success by matching the right hardware and software platform options for your use cases and operational needs.
  • How to design infrastructure to overcome unnecessary bottlenecks inhibiting scalable training performance
  • How to build end-to-end AI development workflows that enable productive experimentation, training at scale, and model refinement.
Join us after the presentation for a live Q&A session.



You will receive an email with instructions on how to join the webinar shortly.

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.


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Tony Paikeday

Director, AI Systems and Data Science Platform, NVIDIA

Tony Paikeday is responsible for the go-to-market for NVIDIA’s DGX portfolio of AI supercomputers and NVIDIA’s accelerated machine learning platform. Tony helps enterprise organizations infuse their business with the power of AI through infrastructure solutions that enable data-based insights. Prior to NVIDIA, Tony worked at VMware and Cisco, where he built data center solutions and end-user computing applications for enterprise. He started his career as a manufacturing engineer at Ford Motor Company, after receiving his B.S. in engineering from the University of Toronto, Ontario.

Balázs Lóránd PhD

Head of Deep Learning Data and Infrastructure Group, Continental Automotive

Balázs is responsible for providing compute capacity and data services for various deep learning algorithm developer teams of Continental ADAS Business Unit. In his current position he is building up teams of data engineers, data managers, infrastructure engineers and project managers to enable deep learning by implementing cutting edge solutions. Prior to this Balázs worked on several machine learning and analytics projects as a consultant / analyst, later he joined T-Systems Hungary as a senior data science consultant and project manager and worked on innovative real time location analytics and text analytics projects. Later he also worked in chief of staff position at the SW development branch of the company. He was also the leader of T-Systems Hungary’s Big Data and analytics team and was responsible for coordination of relevant presales and implementation projects. Previously Balázs also worked for GE Digital as a technical project manager to support the establishment of corporate data lake for sourcing analytics. Balázs graduated as an Economist (MSc), later he received his PhD degree in Economics from the University of Pécs (Hungary).

Venkatesh Ramanathan

Director of Data Science, PayPal

Venkatesh is Director of Data Science at PayPal where he is leading research initiatives in large scale AI and machine learning. He has over 25+ years of professional experience in AI research and building scalable engineering solutions. Venkatesh holds a Ph.D. degree in Computer Science with specialization in Machine Learning and Natural Language Processing (NLP) and had worked on various problems in the areas of Anti-Spam, Phishing Detection, Face Recognition and Payment Fraud.

Zachary Fragoso

Manager of Data Science, Domino's

Zack is a manager of data science and AI at Domino's in Ann Arbor, Michigan. His team develops ML/AI applications that ensure over 3 million global customers have a great experience every day.

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Webinar: Description here

Date & Time: Wednesday, April 22, 2018