Surprising progress has been made on Machine Learning algorithms and infrastructures recently. These techniques are being used to solve a lot of previously un-solvable problems. However, not all problems are directly ML-applicable. Some needs to be divided and transformed properly before the powerful ML technique can be applied to it. In this session, I’ll talk about what kinds of “black magic” we applied to the data problems we are facing daily at Mattermark, so that we can solve them in graceful and scalable way using Machine Learning.