I wish my company was cool enough such that our internal tools (even if they're technically complex) would be of interest to people :(
Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
21–27 of 27 posts
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#22Earlier quoted context omitted.
I agree with you, but I also think there’s room to point out that Uber has lost credibility and it wouldn’t be surprising or inconsistent if it was just a PR post for recruiting hype. Without details (not mere surface comments) on how it is differentiated from the many other available solutions and deep dives into what use cases it is specifically better suited for, it seems reasonable to treat it with a lot of skept…
Skepticism is one thing, lack of civility is quite another (and I'm sorry, but calling stuff "garbage" in such an off-hand way is clearly uncivil to me, especially when shallow dismissals are expressly pointed out as a problem). See brylie's comment in this thread for how to do it right - take that, add a stronger caveat about this tool's proprietary status, in contrast to so much other stuff in this space being FLOS…
I didn’t write a comment calling anything garbage. In what way did my comment degrade discussion?
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#23Earlier quoted context omitted.
Skepticism is one thing, lack of civility is quite another (and I'm sorry, but calling stuff "garbage" in such an off-hand way is clearly uncivil to me, especially when shallow dismissals are expressly pointed out as a problem). See brylie's comment in this thread for how to do it right - take that, add a stronger caveat about this tool's proprietary status, in contrast to so much other stuff in this space being FLOS…
I didn’t write a comment calling anything garbage. In what way did my comment degrade discussion?
I believe they’re referring to 'Michaelanjello’s comment upthread:
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#24Earlier quoted context omitted.
I didn’t write a comment calling anything garbage. In what way did my comment degrade discussion?
I believe they’re referring to 'Michaelanjello’s comment upthread: https://news.ycombinator.com/item?id=18692801
Well my comment itself was downvoted, so I don’t agree it was a focus on the original comment.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#25I manage a ML team and the article has several ‘borrowable’ ideas, the primary idea supporting model builders working on their local Linux device and simplifying the process of transferring locally built models to a scalable platform. If we were starting today to implement our in house system I might just use Ubber’s system as a checklist for features to implement. Not too off topic: after mostly building and trainin…
Uber's software doesn't exist in the public realm, and so there is nothing to learn from it. It is imaginary fluff. I have posted a list of the ones that do actually exist in another comment. And you should've at least got a 1080.
It actually gives a lot of idea and validation for concepts that I was thinking about for my project https://github.com/polyaxon/polyaxon, namely for going from trained model to serving those models on other platforms in a seamless way. I also think that high-level articles like this one are really good for people who are trying to build internal tools around ML/AI ops, people can get more or less ideas about what and how other companies are managing their ML pipelines and how they impact the productivity of their teams.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#26Anyone working at Uber that can highlight the non trivial differences between this pyML Michelangelo and Kubeflow? I am very interested in hearing first hand opinions about pros and cons
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#27Earlier quoted context omitted.
Uber's software doesn't exist in the public realm, and so there is nothing to learn from it. It is imaginary fluff. I have posted a list of the ones that do actually exist in another comment. And you should've at least got a 1080.
It actually gives a lot of idea and validation for concepts that I was thinking about for my project https://github.com/polyaxon/polyaxon , namely for going from trained model to serving those models on other platforms in a seamless way. I also think that high-level articles like this one are really good for people who are trying to build internal tools around ML/AI ops, people can get more or less ideas about what a…
You should be getting ideas from actual open source projects that actually exist, not from imaginary/fake ones. There are numerous serving packages that actually exist and offer ideas.