It's an open issue : https://issues.apache.org/jira/browse/SPARK-4587
That's being said, you can workaround the issue by serializing the Model
(simple java serialization) and then restoring it before calling the
predicition job.
Best Regards,
On 22/10/2015 14:33, Sebastian Kuepers wrote:
>
Hi Sebastian,
You can save models to disk and load them back up. In the snippet below
(copied out of a working Databricks notebook), I train a model, then save
it to disk, then retrieve it back into model2 from disk.
import org.apache.spark.mllib.tree.RandomForest
> import
Hey,
I try to figure out the best practice on saving and loading models which have
bin fitted with the ML package - i.e. with the RandomForest classifier.
There is PMML support in the MLib package afaik but not in ML - is that correct?
How do you approach this, so that you do not have to fit
