Max Cantor is a Software Engineer of Machine Learning at Condé Nast. He designs and maintains machine learning platforms that scale to thousands of models and terabytes of data in a production environment. He is an Insight Data Engineering Fellow, received a Masters degree in Cognitive Psychology and Cognitive Neuroscience at the University of Colorado Boulder, and graduated with Honors at the University of Michigan with a Bachelors degree in Psychology.
Condé Nast is a global leader in the media production space, housing iconic brands such as The New Yorker, Wired, Vanity Fair, and Epicurious, among many others. Along with our content production, Condé Nast invests heavily in companion products to improve and enhance our audience's experience. One such product solution is Spire, a framework for user segmentation and targeted advertising for over a hundred million users. Spire maintains thousands of models trained weekly or monthly, and user propensity scores are derived from these models daily. We have developed considerable tooling to manage these data flows along with their respective metrics and audit logs. This talk will outline the requirements of a production ML software product, how our tooling meets these needs, and review what we have learned throughout Spire's development.
