Hi Sonal,
Thanks a lot for this suggestion. I presume it might indeed be possible
to use MLFlow for this purpose, but at present it seems a bit too much
to introduce another framework only for storing arbitrary meta-data with
trained ML pipelines. I was hoping there might be a way to do this
natively in Spark ML. Otherwise, I'll just create a wrapper class for
the trained models.
Cheers,
Martin
Am 2021-10-24 21:16, schrieb Sonal Goyal:
Does MLFlow help you? https://mlflow.org/
I don't know if ML flow can save arbitrary key-value pairs and
associate them with a model, but versioning and evaluation etc are
supported.
Cheers,
Sonal
https://github.com/zinggAI/zingg
On Wed, Oct 20, 2021 at 12:59 PM <mar...@wunderlich.com> wrote:
Hello,
This is my first post to this list, so I hope I won't violate any
(un)written rules.
I recently started working with SparkNLP for a larger project.
SparkNLP in turn is based Apache Spark's MLlib. One thing I found
missing is the ability to store custom parameters in a Spark pipeline.
It seems only certain pre-configured parameter values are allowed
(e.g. "stages" for the Pipeline class).
IMHO, it would be handy to be able to store custom parameters, e.g.
for model versions or other meta-data, so that these parameters are
stored with a trained pipeline, for instance. This could also be used
to include evaluation results, such as accuracy, with trained ML
models.
(I also asked this on Stackoverflow, but didn't get a response, yet:
https://stackoverflow.com/questions/69627820/setting-custom-parameters-for-a-spark-mllib-pipeline)
Would does the community think about this proposal? Has it been
discussed before perhaps? Any thoughts?
Cheers,
Martin
