Define Once, Evaluate Anywhere: Building Repeatable and Correct Features at Stripe

Kelley Rivoire | Stripe


Feature engineering can produce a never-ending set of gotchas - from bugs in features that aren't defined the same way (or even in the same language) in training versus production to mistakes in recording when a feature or label was actually available that lead to unrealistically predictive models. 
We will discuss the system we built at Stripe leveraging event-ed data to enable model developers to quickly define (and test!) complex and highly predictive features in a single place in code and make them available for both training and real-time scoring eliminating some of these common classes of feature generation errors.

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kelley rivoire

Engineering Manager | Stripe

Kelley is an engineering manager at Stripe, where she leads the data infrastructure group. Previously at Stripe, she led the machine learning infrastructure team, and as an engineer built Stripe's first real-time machine learning evaluation of user risk. Prior to joining Stripe, she completed a PhD at Stanford in Electrical Engineering and worked as a researcher at HP Labs.

Kelley Rivoire Stripe