Abstract: Taking an AI model from the lab to production is extremely challenging. In fact, recent reports and surveys estimate that only 20%-30% of the deep neural modelling attempts find their way to productive deployment. One of the major bottlenecks in the path from the lab to production is the poor latency or throughput performance of these neural models, which immediately translates to excessively high cost-to-serve.
In this talk, we present an innovative solution to this problem, driven by Deci AI’s deep learning platform. Our platform tackles the challenge by using AI-based neural architecture search (NAS). It is capable of crafting and improving almost any given deep neural network, thus allowing networks to achieve production performance grade without compromising accuracy. Our proprietary Automated Neural Architecture Construction engine (AutoNAC) unlocks a whole set of AI opportunities for cloud, on-prem, and edge deployments.
The session will begin with a presentation by Deci’s CEO, Yonatan Geifman, PhD, who will introduce AutoNAC, and provide a peek into its algorithmic principles. Following this, Sefi Bell-Kligler, Deci’s Director of AI, will present an end-to-end technical demo showcasing real-world cases; these examples demonstrate Deci’s platform, featuring its AutoNAC algorithmic optimization, and the associated user journeys.
Bio: Yonatan Geifman is the CEO and a Co-Founder at Deci AI. He granted B.Sc. in mathematics and Computer Science from Ben-Gurion University, and M.Sc. and Ph.D in Computer Science from the Technion - Israel Institute of Technology. During his Ph.D. studies, Yonatan worked as a Research Intern in Google AI, Mountain View.
His research areas include deep learning, uncertainty estimation, active learning and neural architecture search (NAS), and his papers have been published in premier venues such as NeurIPS, ICML, and ICLR.
Dr. Geifman founded Deci with a mission to supercharge AI models for top performance, so they are ready for production at scale.