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

Forrest Iandola (DeepScale)
2:35pm-3:15pm Thursday, September 6, 2018
AI Business Summit, AI in the Enterprise
Location: Continental 4
Secondary topics:  AI in the Enterprise
Average rating: ****.
(4.67, 3 ratings)

What you'll learn

  • Learn how to build, nurture, and grow a full stack AI engineering team


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.

Photo of Forrest Iandola

Forrest Iandola


Forrest Iandola is CEO of DeepScale, a company focused entirely on building perception systems for automated vehicles, drawing on the advances in scalable training and efficient implementation of deep neural networks that emerged from Forrest’s graduate research. DeepScale has a number of engagements with automakers and automotive suppliers. Forrest holds a PhD in electrical engineering and computer science from UC Berkeley, where his research focused on deep neural networks.