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Re:CometML wants to do for machine learning what GitHub did ... by jorgemf on Thursday April 05, 2018 @11:22AM (#16765711)
I would like to use it but I think the price doesn't justify the tool. For a team of 5 people github is $25 a month, you are $745 a month. I do understand a price a bit higher that github but not 30 times more expensive.
Re:CometML wants to do for machine learning what GitHub did ... by shackenberg on Thursday April 05, 2018 @11:42AM (#16765900)
I like, that the price is so high. That makes it a least seem to be a sustainable business. And when you pay your ML people 10k a month, the $25 is less than the coffee they will drink in the office.
Re:CometML wants to do for machine learning what GitHub did ... by jorgemf on Friday April 06, 2018 @12:25AM (#16770863)
145 per user per month, not 25. Not all countries pay 10k a month to their employees
Re:CometML wants to do for machine learning what GitHub did ... by gidim on Thursday April 05, 2018 @11:41AM (#16765890)
Thanks @jorgemf. Keep in mind that $745 also includes unlimited usage of our hyper-parameter optimization service.
Re:CometML wants to do for machine learning what GitHub did ... by maksimum on Thursday April 05, 2018 @02:23PM (#16767544)
Is hyper-parameter optimization a cherry on top or one of the core value propositions?

IME hyper-parameter optimization doesn't require much in terms of implementation effort (e.g. [1]), but requires compute. I would be surprised if a professional ML/DS user were to seriously consider paying for the implementation of the optimization.

[1] https://people.eecs.berkeley.edu/~kjamieson/hyperband.html

Re:CometML wants to do for machine learning what GitHub did ... by XnoiVeX on Thursday April 05, 2018 @12:24PM (#16766305)
Are there any similarities or distinctions between this article (link below) and how your system works to tune hyper-parameters?

https://blog.coast.ai/lets-evolve-a-neural-network-with-a-ge...

Re:CometML wants to do for machine learning what GitHub did ... by gidim on Thursday April 05, 2018 @12:45PM (#16766513)
This article seems to discuss genetic algorithms which could be used for hyperparam optimization. We use another method called Bayesian (GP) hyperparam optimization. According to our internal benchmarks and academic research Bayesian methods outperform genetic algorithms. Another thing to keep in mind that we automate the entire process for you. You only need to provide a list of parameters you'd like to tune.
Re:CometML wants to do for machine learning what GitHub did ... by jorgemf on Friday April 06, 2018 @12:29AM (#16770881)
That service doesn't justify the price tag for me.