Lifelong learning refers to ML techniques aiming to build models that can continually learn and improve over time, without forgetting priorly learned knowledge. This is in contrast to "stationary" models which are trained once on a given dataset, and never updated; such models may be subject to "model drift" if the distribution of the test/deployment set changes over time.