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
