Sorry for the wrong link, what you should refer is jpmml-sparkml (https://github.com/jpmml/jpmml-sparkml).

Thanks
Yanbo

2016-07-24 4:46 GMT-07:00 Yanbo Liang <ybliang8@gmail.com>:
Spark does not support exporting ML models to PMML currently. You can try the third party jpmml-spark (https://github.com/jpmml/jpmml-spark) package which supports a part of ML models.

Thanks
Yanbo

2016-07-20 11:14 GMT-07:00 Ajinkya Kale <kaleajinkya@gmail.com>:
Just found Google dataproc has a preview of spark 2.0. Tried it and save/load works! Thanks Shuai.
Followup question - is there a way to export the pyspark.ml models to PMML ? If not, what is the best way to integrate the model for inference in a production service ?

On Tue, Jul 19, 2016 at 8:22 PM Ajinkya Kale <kaleajinkya@gmail.com> wrote:
I am using google cloud dataproc which comes with spark 1.6.1. So upgrade is not really an option.
No way / hack to save the models in spark 1.6.1 ?

On Tue, Jul 19, 2016 at 8:13 PM Shuai Lin <linshuai2012@gmail.com> wrote:
It's added in not-released-yet 2.0.0 version.


so i guess you need to wait for 2.0 release (or use the current rc4). 

On Wed, Jul 20, 2016 at 6:54 AM, Ajinkya Kale <kaleajinkya@gmail.com> wrote:
Is there a way to save a pyspark.ml.feature.PCA model ? I know mllib has that but mllib does not have PCA afaik. How do people do model persistence for inference using the pyspark ml models ? Did not find any documentation on model persistency for ml.

--ajinkya