That's good to hear, how does it work in practice? Is it basically running the same training as from scratch, but with only the new data, on a separate machine to produce a new version which is then replacing the old production version? Is part of MLOps starting a new training session each week, checking if the loss function looks ok, and then redeploying it?
I still think of how humans work. We don't get retrained from time to time to improve, we learn continually as we gain experience. It should be doable in at least some cases, like classification where it's easy to tell if a label is right or wrong.
I still think of how humans work. We don't get retrained from time to time to improve, we learn continually as we gain experience. It should be doable in at least some cases, like classification where it's easy to tell if a label is right or wrong.