Yury,

Thanks for driving this. From my side I would suggest looking at Spark MLlib 
borrowing the most frequently used algorithms from there. You've already 
mentioned regression and clustering algorithms, however, it’s reasonable to 
support classifications and decision trees.
http://spark.apache.org/docs/latest/ml-guide.html 
<http://spark.apache.org/docs/latest/ml-guide.html>

Next, according to my observations Ignite ML Lib has to support Ruby and Python 
if we wish the lib to be used by researches and scientists.

Finally, we have to find a better way on how to integrate Java 8 based Ignite 
ML with the rest of the platform. Presently, it’s a pain for Ignite build and 
release processes to treat Ignite ML differently. I propose to make up a 
solution by the time of Apache Ignite 2.1.

—
Denis

> On Apr 21, 2017, at 9:43 AM, Yury Babak <y.ch...@gmail.com> wrote:
> 
> Guys,
> 
> Since the first version of Ignite ML module was merged into ignite 2.0 we
> want to discuss our next steps.
> 
> Currently we think about 3 big areas to explore:
> 
> 1) Regression and clustering algorithms.
> 2) Deep Learning/Neural Networks stuff.
> 3) DSL/scripting support.
> 
> Suggestions/thoughts about these topics (or something else which you think
> we have missed) are welcome here as well as in IGNITE-5029.
> 
> Some details about above topics.
> 
> * First draft of ordinary least squares linear regression is in progress (by
> Artem, IGNITE-5012).
> * Deep learning/other NN stuff: currently Artem is investigating existing
> frameworks like Tensorflow/Encog/etc to find out if we can integrate with
> them somehow or at least define the scope/ideas for API of DL/NN
> functionality we need.
> * Also we think about using Java 8 Nashorn as script engine and possibility
> of build R-like DSL (mostly by me).
> 
> Thanks,
> Yury Babak.
> 
> 
> 
> 
> 
> --
> View this message in context: 
> http://apache-ignite-developers.2346864.n4.nabble.com/Ignite-ML-next-steps-IGNITE-5029-tp17096.html
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