Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
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Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#2Pretty slick, but more impressive is their well written overview of the tool.
It has some interesting model def features: The model interfaces abstract away where and how the model will be deployed so that the user can focus on what they understand best: the data going in and out of the model.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#3As it stands, this article strikes me more as a recruitment device than something useful to ML practitioners outside of Uber. I'm hopeful Uber will open-source parts of the Michelangelo stack, so it will be a contribution to the broader community.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#4Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me.
Meanwhile, here is a list of open source ML deployment packages:
https://github.com/oracle/graphpipe
https://github.com/eliorc/denzel
https://github.com/tensorflow/serving
https://github.com/ucbrise/clipper
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#5Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me. Meanwhile, here is a list of open source ML deployment packages: https://github.com/oracle/graphpipe https://github.com/eliorc/denzel https://github.com/tensorflo…
FWIW, the "Guidelines" ask you to please not dismiss work out of hand. Appearances matter, too - you can point out that a tool is proprietary and has lots of plausible FLOSS alternatives without being uncivil about it ("garbage").
Edited: Commenting about the voting, especially in the way you did here, is inappropriate too. We're here for substantive discussion.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#6Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me. Meanwhile, here is a list of open source ML deployment packages: https://github.com/oracle/graphpipe https://github.com/eliorc/denzel https://github.com/tensorflo…
FWIW, the "Guidelines" ask you to please not dismiss work out of hand. Appearances matter, too - you can point out that a tool is proprietary and has lots of plausible FLOSS alternatives without being uncivil about it ("garbage"). Edited: Commenting about the voting, especially in the way you did here, is inappropriate too. We're here for substantive discussion.
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 skepticism.
But to be clear, I totally agree in terms of the degree and tone. I don’t think the parent comment you responded to was “uncivil” in any way, but overly dismissive instead of just noting to be skeptical.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#7Earlier quoted context omitted.
FWIW, the "Guidelines" ask you to please not dismiss work out of hand. Appearances matter, too - you can point out that a tool is proprietary and has lots of plausible FLOSS alternatives without being uncivil about it ("garbage"). Edited: Commenting about the voting, especially in the way you did here, is inappropriate too. We're here for substantive discussion.
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 FLOSS, and it would make your point quite well without degrading discussion.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#8Pretty slick, but more impressive is their well written overview of the tool. It has some interesting model def features: The model interfaces abstract away where and how the model will be deployed so that the user can focus on what they understand best: the data going in and out of the model.
Are you serious? There is no code for us, so there is no tool! The article is as good as fake news.
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#9[deleted]
Re: Michelangelo PyML: Uber’s Platform for Rapid Python ML Model
#10I 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 training models on remote compute (both at work and for my at home learning projects), I recently bought a fast laptop for home use that has a 1070 GPU and the experience of using a local device with a GPU makes me regret many years of SSHing to remote compute or using Jupyter remotely for machine learning development. I mention this as a lesson learned that may be helpful.