Streaming data holds the key to changing trends and actions. It requires an elastic, message-based architecture that adjusts to data and processing demands so as to allow demanding machine learning methods to continuously update their models and hence their predictions. This also allows ensemble approaches that allow multiple machine learning methods to cooperate efficiently. We outline and demonstrate various machine learning methods including statistical and neural network approaches and their incorporation into a streaming based architecture. Code available on GitHub.
