Building a scalable machine vision pipeline

Discovery on Pinterest is all about finding things you love, even if you don't know at first what you're looking for. The Visual Discovery engineering team at Pinterest is tasked with building technology that will help people to continue to do just that, by building technology that understands the objects in a Pin's image to get an idea of what a Pinner is looking for. Over the last year we've been building a large-scale, cost-effective machine vision pipeline and stack with widely available tools with just a few engineers. Today we're sharing some new technologies we're experimenting with, as well as a white paper, accepted for publication at KDD 2015, that details our system architecture and insights from these experiments and makes the following contributions: It used to be that if a Pin had never before been saved on Pinterest, we weren't able to provide Related Pins recommendations. This is because Related Pins were primarily generated from traversing the local "curation graph," the tripartite user-board-image graph evolved organically through human curation.