Abstract: Machine learning on a heterogeneous event data stream using an exploit-explore model. The heterogeneous event data stream may include any number of different data types. The system featurizes at least part of the incoming event data stream in accordance with a common feature dimension space. The resulting stream of featurized event data is then split into an exploration portion and an exploitation portion. The exploration portion is used to performed machine learning to thereby advance machine knowledge. The exploitation portion is used exploit current machine knowledge. Thus, an automated balance is struck between exploitation and exploration of an incoming event data stream. The automated balancing may even be performed as a cloud computing service.