1. A method comprising: transforming in real time raw event data, representing a sequence of events and captured from multiple sources in a computer network, into a stream of event feature sets without a known end-point to the stream, where the raw event data includes time-stamped machine data; computing in real-time a score by processing a time slice of the stream of event feature sets through an active version of a machine learning model, wherein the time slice includes a most recent event feature set in the stream of event feature sets; training, in parallel with said processing the time slice and responsive in real-time to said transforming the raw event data, a non-active version of the machine learning model with the time slice that is being processed through the active version for scoring, wherein the machine learning model is trained to represent a particular entity involved in a computer network activity represented by the raw event data; identifying, by comparing the score against a threshold, a security-related anomaly or a security-related threat to enable remediation of the security-related anomaly or the security-related threat in the computer network as the stream of event feature sets is processed in real-time; determining that the non-active version of the machine learning model is ready for active deployment based on at least one of: a number of event feature sets that have been used to train the non-active version, length of time that the non-active version has been in training, or whether a model state of the non-active version is converging; and live-swapping in the non-active version as the active version to compute another score by processing a subsequent time slice from the stream of event feature sets through the live-swapped-in active version of the machine learning model.