What If AI Could Craft the Next Generation of AI
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.
3 Key Takeaways:
- The challenges, solutions, and opportunities associated with building and maintaining AI models that are ready for production.
- The “Algorithmic Complexity” and how to overcome it for faster deep learning inference at scale.
- The fundamental principles for leveraging AI to build better deep learning models - a paradigm shift reinvented by Deci.
Yonatan Geifman is co-founder and CEO of Deci, the deep learning company dedicated to transforming the AI lifecycle. He co-founded Deci after completing his PhD in computer science at the Technion-Israel Institute of Technology. His research focused on making Deep Neural Networks (DNNs) more applicable for mission-critical tasks.
During his studies, Yonatan was also a member of Google AI’s MorphNet team. His research has been published and presented at leading conferences across the globe, including the Conference on Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the International Conference on Learning Representations (ICLR).