These are slides presented at MLconf in San Francisco, November 14, 2014. I share the approach to real-time machine learning for recommender systems developed at if(we). We achieve rapid iterative cycles by adhering to a strict approach to structuring and accessing our data, as well as to building the online features that comprise our models. These developments support teams of data scientist and data engineers, who work together to solve complex recommendation problems. We also introduce the Antelope Realtime Events framework, an open source demonstration application which derives from our scalable proprietary software stack.