Hi List,
The topic of prediction latency seems to pop up from times to times, either
on the list or on SO [1].
I'm pondering the idea of creating a performance benchmark in the spirit of
the out-of-core example.
This benchmark could be used to compare and track prediction time of a few
popular algorithms in sklearn.
It could be used also to showcase to new users what they can expect from
the different algorithms and help them choose a solution that fits their
requirements.
I don't know if other machine learning toolkits have this kind of
benchmarks (and do we already have some various pieces out there ?), but I
think it would be a great feature for the "enterprise" users who often need
to choose a toolkit in a limited amount of time and stick to it during the
life of the project.
It could also give water to the idea that Python can/should be fast ;)
Any thoughts on that ?
Eustache
[1]
http://stackoverflow.com/questions/11295755/gradient-boosting-predictions-in-low-latency-production-environments/13137344#13137344
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