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From Xiangrui Meng <men...@gmail.com>
Subject Re: Status of MLLib exporting models to PMML
Date Tue, 11 Nov 2014 18:07:21 GMT
Vincenzo sent a PR and included k-means as an example. Sean is helping
review it. PMML standard is quite large. So we may start with simple
model export, like linear methods, then move forward to tree-based.
-Xiangrui

On Mon, Nov 10, 2014 at 11:27 AM, Aris <arisofalaska@gmail.com> wrote:
> Hello Spark and MLLib folks,
>
> So a common problem in the real world of using machine learning is that some
> data analysis use tools like R, but the more "data engineers" out there will
> use more advanced systems like Spark MLLib or even Python Scikit Learn.
>
> In the real world, I want to have "a system" where multiple different
> modeling environments can learn from data / build models, represent the
> models in a common language, and then have a layer which just takes the
> model and run model.predict() all day long -- scores the models in other
> words.
>
> It looks like the project openscoring.io and jpmml-evaluator are some
> amazing systems for this, but they fundamentally use PMML as the model
> representation here.
>
> I have read some JIRA tickets that Xiangrui Meng is interested in getting
> PMML implemented to export MLLib models, is that happening? Further, would
> something like Manish Amde's boosted ensemble tree methods be representable
> in PMML?
>
> Thank you!!
> Aris

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