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 <arisofala...@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 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
