Hi Rishabh, I have a similar use-case and have struggled to find the best solution. As I understand it 1.6 provides pipeline persistence in Scala, and that will be expanded in 2.x. This project https://github.com/jpmml/jpmml-sparkml claims to support about a dozen pipeline transformers, and 6 or 7 different model types, although I have not yet used it myself.
Looking forward to hearing better suggestions? Steve On Fri, Jul 1, 2016 at 12:54 PM, Rishabh Bhardwaj <rbnex...@gmail.com> wrote: > Hi All, > > I am looking for ways to deploy a ML Pipeline model in production . > Spark has already proved to be a one of the best framework for model > training and creation, but once the ml pipeline model is ready how can I > deploy it outside spark context ? > MLlib model has toPMML method but today Pipeline model can not be saved to > PMML. There are some frameworks like MLeap which are trying to abstract > Pipeline Model and provide ML Pipeline Model deployment outside spark > context,but currently they don't have most of the ml transformers and > estimators. > I am looking for related work going on this area. > Any pointers will be helpful. > > Thanks, > Rishabh. >
