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.apache.org>
> Oggetto: Re: ML
16 21:55
A: "Simone Miraglia"
Cc: "User"
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. Some also use:
https://github.com/jpmml/jpmml-sparkml
It depends on the model, e.g
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 this
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 v
