4 ms·
Unsolved Problems in MLOps
- rho138 3mo ago(PDF)
- juancn 3mo agoTLDR AI Summary of the thing: https://gist.github.com/juancn/9bc654ccffbba113271a068a2d854529 https://gist.github.com/juancn/9bc654ccffbba113271a068a2d854... (I found the flourished language of the original a bit too much for my taste)
- grosswait 3mo agoThanks, appreciated. I read about 2/3 or the article. I like the content, and appreciate that it doesn’t scream “written by AI”, but it did get long winded in places.
- occupant 3mo agoNon-pdf link: https://queue.acm.org/detail.cfm?id=3762989 https://queue.acm.org/detail.cfm?id=3762989
- ks2048 3mo agoSummary (at end of PDF): 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 In some cases, they are using outdated techniques; in others, approaches that only work for now; and every so often they are doing nothing at all in order to meet significant operational, technical,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.
- jchatwall82 3mo ago[flagged]