In the new era of the extreme data economy, companies across industries are turning data into an asset above and beyond any product or service they offer. But unprecedented agility is required to operationalize AI, keep business in motion, and succeed in this post-big data era. To enable this level of agility, companies are turning to instant insight engines that are powered by NVIDIA GPUs, bringing unparalleled speed, streaming data analysis, visual foresight, and streamlined machine learning all in the same data platform.
Daniel Raskin and Jonathan Greenberg explain what the extreme data economy is about and how machine learning advances along with accelerated parallel computing will play a key role in translating data into instant insight to power business in motion, using a real-world environmental analytics problem—the collapse of domestic honey bee hives—as an example. You’ll see how real-time data collection, machine learning, and analytics visualization help rescue the honey bee with IoT streaming sensor data in real time, edge-computing solutions that process image inferencing algorithms on still images and live video in the field, and a GPU-accelerated database engine for real-time data ingestion, query, machine learning, and visualization. You’ll also learn how adaptable and accessible these cutting-edge technologies have become to rapidly optimize, validate, and deploy trained neural networks—all while helping to rescue the honey bee in the real world.
This session is sponsored by Kinetica.
Daniel Raskin is the chief marketing officer at Kinetica, where he is responsible for leading all aspects of worldwide marketing. Daniel has approximately 20 years of experience building brands and driving product leadership. Previously, he was vice president of marketing and senior vice president of product management at digital identity management company ForgeRock, chief identity strategist at Sun Microsystems, and a senior executive at McGraw-Hill, NComputing, and Agari. Daniel holds a master’s degree in international management from Thunderbird School of Global Management and a master’s degree in publishing from Pace University.
Jonathan Greenberg is a senior solutions engineer at Kinetica, where he revels in the pace of change in software and hardware for analytics, the introduction of the GPU-enhanced database, and the business impact around convergence of ML and BI that Kinetica brings to this challenged big data (science) space. Jonathan has spent the last three years at startups exploring modern and innovative analytic technologies and platforms. In his 20-year career conceiving, developing, and delivering effective business intelligence solutions for a broad range of industries, Jonathan has worked for Cognos, BMW, and IBM.
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