looking for a more
> general purpose approach.
>
> Any other thoughts?
> Best
> Simone
> Da: Peyman Mohajerian <mailto:mohaj...@gmail.com>
> Inviato: 20/07/2016 21:55
> A: Simone Miraglia <mailto:simone.mirag...@gmail.com>
> Cc: User <mailto:user@spark.a
gt;
Inviato: 20/07/2016 21:55
A: "Simone Miraglia" <simone.mirag...@gmail.com>
Cc: "User" <user@spark.apache.org>
Oggetto: Re: ML PipelineModel to be scored locally
One option is to save the model in parquet or json format and then build your
own prediction code
One option is to save the model in parquet or json format and then build
your own prediction code. Some also use:
https://github.com/jpmml/jpmml-sparkml
It depends on the model, e.g. ml v mllib and other factors whether this
works on or not. Couple of weeks ago there was a long discussion on
Hi all,
I am working on the following use case involving ML Pipelines.
1. I created a Pipeline composed from a set of stages
2. I called "fit" method on my training set
3. I validated my model by calling "transform" on my test set
4. I stored my fitted Pipeline to a shared folder
Then I have a
