Building Machine Learning Infrastructure at Netflix and beyond

Savin Goyal on Metaflow and the state of ML infrastructure.

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Savin Goyal is CTO and co-founder of Outerbounds, a startup building infrastructure to help teams streamline how they build machine learning applications. Prior to starting Outerbounds, Savin and team worked at Netflix, where they were instrumental in the creation and release of Metaflow, an open source Python framework that addresses some of the challenges data scientists face around scalability and version control.

Savin Goyal:

Netflix has always sort of stuck to the idea that better quality data will result in better quality models. So that was the focus. But then, of course, you know, Netflix also operates at the bleeding edge of machine learning. So there were many areas where we were using deep learning, but I wouldn’t say that deep learning was the most popular choice.

The machine learning universe is really fast moving. So how can we make sure that we’re not making a bet, that would hinder our progress, two years or four years further down the line. Deep learning is super popular, but tomorrow there could be a new way of doing machine learning. How can we make sure that we are providing that freedom of choice to our data scientists, and not really encumbering on that.

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