Building a feature library for machine learning at iZettle
When we started building features for our first machine learning models at iZettle, we quickly realized we didn't want to reinvent the wheel for every model we built. Instead, we created a shared feature building framework, which over time has evolved into a core part of our machine learning infrastructure. In this talk, I will explain why you might want to create a feature library, suggest some important trade-offs to consider and share how we first built the simplest solution possible and then iterated from there.
Rebecka is the Machine Learning Lead at iZettle, a member of the PayPal family, that is on a mission to help small businesses succeed in a world of giants. She manages a team of data scientists and machine learning engineers working on problems ranging from fraud detection and credit scoring, to marketing optimization and lead generation. Rebecka is also one of the founders of Women in Data Science Sweden, a network for current and aspiring female data scientists that arranges technical conferences and events.
