@Paco: I understand that most promising for me to put effort in understanding for in deploying models in the spark enviroment would be augustus and zementis right?
actually as you mention I would have both direction of deploying. I have already models which I could transform into pmml and I also think in building more models in time using spark... or other model engines in the hadoop field When I read about mllib and mlbase I got very interested in it because it seems to handle some aspects of my actual challenge (building arround 1000 models, administrate 1000 models, calculate arount 2 billions scores each week) but with the administartion stuf I am so so sure about. also i find that one need to put in the field (spark, mllib, mlbase, ...) some effort into the transparency of the models. as long you just build a recomender system you probably dont need something like that but as you mention... there are a lot of departments where analysts are building the models because the risk to spend millions of money in a wrong place beause of the model which wasnt proofed carefully... is simply to high for the managers .... is there actually a direction of administration of scores in the spark/mllib/mlbase field?. I mean somthing like a) description of the score model, training data set, target variable, for what for b) quality check, actual performance in comparison with other models, c) version control system d) indicator if the score is activ or not e) for specificily which action (for instance which website, wich customer group, wich country,...) a commercial product which is in a way compareable would the model manager from sas hey guys.. in anyway I will get involved in this field. It looks so promissing ps: think about integrating a mip solver! because you can not handle every thing with a statistical model. in business you have quite often discrete optimization problems when you try to manage your business with prediction models :-) -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/pmml-with-augustus-tp7313p7353.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
