Fueled by Big Data. Driven by Deep Insights. Powered by GPUs.

AI is transforming the analytics ecosystems and creates a different set of opportunities on enterprises:

1) how companies transform their data to gain valuable insights,

2) how the most sought-after talents would maximize their productivity, and benefit from the best tools,

3) how AI-ready analytics can’t wait, as minutes of delay may cause millions in business losses.

The high compute power, massively parallel processors and high-speed memory of GPUs power an entire ecosystem of AI powered analytics, machine learning, and deep learning partners. In this event, our ecosystem partners will take you through how customers are using NVIDIA GPU-accelerated technologies in finance, e-commerce, and media & entertainment industries.

Agenda

2:00 - 2:30 Accelerated Analytics Landscape and Future Direction (Jim McHugh) + NVIDIA GPU Cloud Demo (Gunter)
2:30 - 3:10 Kinetica: GPU accelerated insights engine on Digital Transformation and Business Risk (Paul)
3:10 – 3:50 MapD: GPU-accelerated SQL engine and visualization system (Todd)
3:50 - 4:00 Coffee Break
4:00 - 4:45 H2O Driverless AI: GPU-Accelerated AutoML for Enterprise (Joe Chow)
4:45 - 5:00 Scaling for Speed and Accuracy in Deep Learning (Gunter)
5:00 - 6:00 Reception

Speakers joining us are:

Jim McHugh, NVIDIA VP and GM of AI Systems who will share the tremendous momentum in the year of 2017 and the direction of accelerated analytics in the year ahead.

Paul Appleby, CEO of Kinetica, who will explain the why a next-gen data platform powered by NVIDIA GPUs is necessary to meet the increasing analytical demands from the modern enterprise. Paul will share some real-world customer use cases and also demonstrate how the Kinetica data platform can be used for advanced analytics on massive datasets to derive truly real-time insights.

Todd Mostak, founder and CEO of MapD Technologies, who will explain MapD’s GPU-accelerated visual analytics can dramatically accelerate feature engineering on large datasets.Todd will visually explore a multi-million row dataset containing more than 260 features on consumer demographics and purchasing preferences in e-commerce. In minutes, Todd will identify the strongest variables with which to train a segmentation model.

Joe Chow, Data Scientist of H2O.ai, who will explain how H2O.ai’s Driverless AI platform enables automatic feature engineering, machine learning and interpretability all with a few clicks to achieve Kaggle grandmaster level accuracy to solve enterprise use-cases like customer churn, fraud, cyber security etc.

“GPU-Accelerated Analytics Ecosystem” Roadshow - Munich