Good machine learning is built on infrastructure but many startups don't have the bandwidth or resources to build this foundation while scaling. It's difficult to prioritize the pieces of ML Infrastructure that data scientists and engineers need to be productive and successful when the scale of these projects can be months or years for small teams of engineers.
The dividends are large down the road but the cost of pursuing infrastructure that doesn't work or doesn't solve the right problems can leave a team months down the road without necessary progress. This talk focuses on the foundation that any good machine learning system is built on and the elements of ML infrastructure to focus on first.
Spencer is a data scientist at Branch International, a leading financial app in emerging markets that uses machine learning to provide financial access to millions of customers worldwide. Spencer focuses on applying advance machine learning to optimize credit decisions and build infrastructure to support Branch's customers. Previously, Spencer held roles at Nest and Qualcomm. He holds a Masters of Science in Computer Engineering from Carnegie Mellon.
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