We have been on a decade long quest to make online interactive learning, a routine fact of life for programmers everywhere. Online learning systems can react quickly to changes in behaviour and have wide ranging applications such as fraud detection, advertising click-through rate prediction, etc. Doing this well has required fundamental research and development of one of the most popular open-source online learning algorithm systems (http://hunch.net/~vw ). Most recently, we have also created a system (http://aka.ms/mwt, http://ds.microsoft.com ) which automates exploit-explore strategies, data gathering, and learning to create stable and routinely useable online interactive learning. For well-framed problems such as content personalization sophisticated interactive online machine learning is now usefully available to programmers. For data scientists who can program, this is a fantastic new tool for solving previously unsolvable problems. As an example: How do you interpret an EEG signal so a user can type?