As discussed at the beginning of this article, the excitement with AI is carrying us along in a big wave, but the practitioners whose job it is to make this all work are scrambling behind the scenes, often more in dread than excitement. In some cases, they are using outdated techniques; in others, approaches that only work for now; and every so often they are doing nothing at all in order to meet significant operational, technical, and business challenges.
In MLOps terms, it sometimes feels that we are using older paradigms to manage a thoroughly new situation, and it’s not entirely clear that we really see it like this. We should be casting about for either a better paradigm or a better patching-up of the existing paradigms than is available today. Regardless, we hope that the summary of the problems presented here is a useful stimulant to people attempting to think about them more holistically and, hopefully, helps to provide some answers.
https://gist.github.com/juancn/9bc654ccffbba113271a068a2d854...
(I found the flourished language of the original a bit too much for my taste)