Lessons From Deploying Deep Learning To Production

Peter Gao, an early engineer at Cruise, reflects on his experience deploying deep learning models into production.

Lessons From Deploying Deep Learning To Production

When I started my first job out of college, I thought I knew a fair amount about machine learning. I had done two internships at Pinterest and Khan Academy building machine learning systems. I spent my last year at Berkeley doing research in deep learning for computer vision and working on Caffe, one of the first popular deep learning libraries. After I graduated, I joined a small startup called Cruise that was building self-driving cars. Now I’m at Aquarium, where I get to help a multitude of companies deploying deep learning models to solve important problems for society.

Over the years, I got the chance to build out pretty cool deep learning and computer vision stacks. There are a lot more people using deep learning in production applications nowadays compared to when I was doing research at Berkeley, but many problems that they face are the same ones I grappled with in 2016 at Cruise. I’ve learned a lot of lessons about doing deep learning in production, and I'd like to share some of those lessons with you so you don’t have to learn them the hard way.