Three Lessons Learned in Building a Successful AI Inferencing Toolkit—from the OpenVINO Architect


Date:  Friday, May 28

Start Time: 11:00 am

End Time: 11:30 am

We started with a simple vision in mind: “How might we get the best deep learning performance with real-world solutions deployed into production?” In this session, the lead architect of Intel Distribution of OpenVINO toolkit, Yury Gorbachev, will discuss the three key lessons learned in building a successful AI inferencing platform. Yury will explore critical topics including low precision optimizations, open-source and the essential components for designing an AI inferencing toolkit architecture.

Track

Session Speakers

  • Yury Gorbachev is a Senior Principal Engineer and Lead Architect for the OpenVINO toolkit at Intel. Yury’s interests are in embedded vision platforms and deep learning. Yury was previously a Principal Engineer at Itseez (acquired by Intel), responsible for embedded platforms and automotive algorithm deployment. Prior to Itseez, Yury was CTO of a Mera Networks business unit focused on embedded solutions and platforms. Yury received a Master of Science degree in math from Nizhny Novgorod State University and has more than 10 years of experience in mobile and embedded systems.


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Three Lessons Learned in Building a Successful AI Inferencing Toolkit—from the OpenVINO Architect