Sep 23–26, 2019

Unleash the power of data at scale (sponsored by Intel)

Jeremy Rader (Intel)
10:00am10:05am Wednesday, September 25, 2019
Location: 3E
Average rating: **...
(2.83, 6 ratings)

Data analytics is the long-standing but constantly evolving science that companies leverage for insight, innovation, and competitive advantage. AI has emerged as a vital tool for reaching higher maturity and scale of data analytics and accelerating the transformation from traditional “operational analytics” that help you understand what happened and why to “advanced analytics” that help you derive real-time insights and predict and plan for the future. AI has unleashed a new era of creativity and ingenuity, but moving solutions from theory to reality and from the one-size-fits-all lab modules development approach to deploying real-world system solutions that are flexible, scalable, and cost-effective have proven to be complex and full of challenges.

Jeremy Rader explores Intel’s end-to-end data pipeline software strategy designed and optimized for a modern and flexible data-centric infrastructure that allows for the easy deployment of unified advanced analytics and AI solutions at scale.

This keynote is sponsored by Intel.

Photo of Jeremy Rader

Jeremy Rader

Intel

Jeremy D. Rader is the senior director of data centric solutions in the Data Center Group, Enterprise and Government Organization at Intel. He leads the group’s strategies and efforts to accelerate E&G customers’ digital transformation by delivering analytics and AI solutions enabled by partners and scaled through the industry. Previously, Jeremy spent 15 years in Intel’s IT organization, culminating in his role as director of business intelligence, where he led Intel’s global team responsible for driving advance analytics and insights across manufacturing, sales a marketing, supply chain, and finance; also at Intel, he held positions in the eBusiness Group and supply chain organization; and he began as a materials services manager in Intel’s manufacturing business.

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