1. A method comprising: computing in real-time a score by processing a stream of events through a first version of a machine learning model, wherein the stream of events corresponds to a time slice and includes time stamped machine data produced by a component within an information environment and reflects activity within the information technology environment, and wherein the machine learning model is capable of being trained to represent a particular entity involved in a computer network activity characterized by the stream of events; training, in parallel with said processing the time slice, a second version of the machine learning model with the time slice that is being processed through the first version for scoring; and performing live-swapping in the second version of the machine learning model to replace the first version of the machine learning model as an active version to compute another score, said live-swapping being based on a determination of whether the second version of the machine learning model is ready for active deployment.