If every application in the world could be easily containerized with an automated process, it might already have happened. We’ve talked before in The New Stack about the many approaches to maintaining state — which, in a less esoteric, more honest-sounding world, is the same thing as “having one’s own database.” For a stateless system, a stateful construct is a pretty neat trick. Or at least for now, it’s your choice of several pretty neat tricks, and maybe a few more not-so-neat ones.
But machine learning is a different order of beast. By implication, something you learn is something you retain. If you can accept the notion that a machine can “learn” anything, then the whole notion is pointless if the machine is also capable of wiping its own slate clean with each iteration. Not even a virtual thing can be real unless it retains the same virtues.
Hear more about Dunning’s vision for “REST for ML” in 'The Intersection Between Machine Learning and Containers,' the latest edition of The New Stack Makers podcast.