Not sure what you mean by serious.
Maybe you could have a look at Meteos[1]. It is a young project but surely
focuses on machine learning.
[1]: https://wiki.openstack.org/wiki/Meteos
Another avenue is to use Storlets for either the learn or prediction
phase where the data resides in Swift.
We are currently adding IPython integration [1] that makes it very
easy to deploy and invoke Storlets from IPython (a data scientists
beloved tool :-), plus [2] is an initial working towards leveraging
Storlets for machine learning.
In few more words: Storlets [3] allow to run a serverless computation
inside Swift nodes, where the computation is done inside a Docker
container. This basically means that you can write a piece of code (in
either Python or Java) upload that code to Swift (as if it was a data
object) and then invoke the uploaded code (called storlet) on your
data (much like AWS Lambda). The nice thing is that the Docker image
where the storlet is executed can be tailored by the admin, and as to
make sure it has, e.g. scikit-learn installed. With such a Docker
image you can write a storlet that would use the sickit-learn
algorithms on swift objects.
[1] https://review.openstack.org/#/c/416089/
[2] https://github.com/eranr/mlstorlets
[3] http://storlets.readthedocs.io/en/latest/
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