OmniSci Highlights from GTC Europe 2018
As a proud NVIDIA partner, we have some exciting highlights to share from GPU Technology Conference (GTC) Europe in Munich last week.
Highlight #1: NVIDIA Introduced RAPIDS Open-Source GPU-Acceleration Platform for Large-Scale Data Analytics and Machine Learning
We’re proud to partner with NVIDIA on RAPIDS, an open-source data analytics and machine learning acceleration platform for executing end-to-end data science training pipelines completely in GPUs.
As founding members of the GoAI consortium, we contributed to the earliest efforts to leverage Apache Arrow as a standard for efficient zero-copy data interchange using a GPU-based dataframe, between different tools in the GPU data science workflow, including OmniSci.
RAPIDS delivers additional libraries that leverage and extend this foundation. We’re naturally planning deeper integration with this toolset and ecosystem as it matures, and very excited to be a part of the initial announcement. Stay tuned for more in upcoming weeks!
“Data scientists use OmniSci on NVIDIA GPUs to accelerate data exploration and feature engineering when creating machine learning models. Now our users can interactively query and visualize data at scale in OmniSci, and then pipe the results into RAPIDS’ open-source libraries, enabling powerful end-to-end data science workflows. Together, NVIDIA and OmniSci make it much faster to build and iterate on models, resulting in increased accuracy and quicker time to deployment.”
- Todd Mostak, CEO and co-founder, OmniSci
Highlight #2: Aaron Williams, OmniSci’s VP of Global Community, spoke about “Driving Telematics Analytics to Extreme Scale and Interactive Speed”
According to a major car manufacturer, modern vehicles are collecting and sharing more than 25 gigabytes of data per hour, from dozens of sensors focused inside and outside the car. Compound that rate of collection across the growing fleets of connected vehicles, and the automotive industry is facing a stiff new challenge: making hundreds of billions of location-intelligent data points comprehensible, actionable, and predictive.
OmniSci's extreme analytics platform are uniquely capable of solving this problem, with orders-of-magnitude faster SQL queries, and full-fidelity rendering on the GPU. In this talk, Aaron Williams used a real-world example to share best practices for analyzing a large dataset of driving behavior, to lower risk and cultivate better drivers.
Look out for the recording on GTC On-Demand. And if you want to learn more about how OmniSci is used for the automotive industry, check out Volkswagen's talk featuring OmniSci at GTC Europe in 2017 about visualizing and interrogating black box AI models, or our solutions here on our website.
Highlight #3: SCAN UK became OmniSci’s First European Reseller
We are proud to announce that we have selected SCAN as our first reseller in Europe. SCAN provides a comprehensive ecosystem of NVIDIA GPU computing, flash storage solutions and complementary data engineering and visualisation solutions.
Last week, SCAN exhibited back-to-back with OmniSci at GTC Europe, where they demoed and displayed the power of our Extreme Analytics Platform.
This partnership allows Scan to deliver large-scale data management, business intelligence analytics and visualisation features to its customers taking the next steps in their AI journey – all in real time. When combined with appliances such as NVIDIA systems, offering up to 16x NVIDIA Tesla V100 Tensor Core GPUs, OmniSci provides unparalleled performance to deliver rapid insight into datasets.
Contact SCAN for UK & EU OmniSci Sales:
+44 (0)1204 474210
Find us at the next GTC event:
OmniSci be GTC DC October 22-24, 2018
- Get a live OmniSci demo at Booth #206
- Attend our talk: “Fighting the Opioid Crisis through Extreme Analytics”
- Date: Wednesday, 10/24/2019
- Time: 11:30 AM - 12:20 PM EST
- Room: Hemisphere B
- We’ll be demonstrating how analytics at extreme speed and scale can help data scientists and analysts rapidly extract fresh insights from open and publicly available datasets related to the opioid health crisis.