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Lessons From Deploying Deep Learning to Production


Aquarium CEO Peter Gao poses with an early Cruise self-driving car.

"The vast majority of problems with model performance can be solved with data, but there are certain issues that can only be solved with changes to the model code." -Peter Gao

Credit: Peter Gao

Peter Gao is the co-founder and CEO of Aquarium, and he previously worked on machine learning for self-driving cars, education, and social media.

In an article, Peter Gao discusses his experiences deploying deep learning into production environments. "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," Gao writes.

"I used to think that machine learning was about the models. Actually, machine learning in production is about pipelines. One of the best predictors of success is the ability to effectively iterate on your model pipeline. That doesn't just mean iterating quickly, but also iterating intelligently...otherwise you end up with a pipeline that produces bad models very quickly."

From The Gradient
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