Executive Briefing: How to develop a full stack deep learning team

Description

Historically, AI has been an algorithm-centric field. However, with the rise of deep neural networks (DNNs), it’s now the case that large-scale data, novel DNN models, and efficient software and hardware infrastructure are all key to success. The best outcomes often come from teams who understand the full stack, from low-level hardware for DNNs to high-level applications of DNNs. Full stack DNN teams are able to make big-picture trade-offs in the development of data, models, and infrastructure, leading to practical solutions that exhibit unprecedented levels of accuracy, speed, and energy efficiency.

Forrest Iandola shares an approach for developing a full stack AI team that can use all of these diverse skills to execute on industrial-scale AI problems. Forrest begins by defining the full stack of skills and technologies that go into DNN engineering before outlining a playbook for managers who want to build, coach, and grow a full stack DNN engineering team. This playbook draws on lessons Forrest learned firsthand at UC Berkeley, Microsoft Research, and DeepScale. Forrest concludes by offering advice on how a generalist or specialist engineer can engage with a full stack DNN engineering team and describes a path to becoming a full stack DNN engineer.