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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:21:19 UTC
[jira] [Updated] (SPARK-7412) Designing distributed prediction
model abstractions for spark.ml
[ https://issues.apache.org/jira/browse/SPARK-7412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-7412:
--------------------------------
Labels: bulk-closed (was: )
> Designing distributed prediction model abstractions for spark.ml
> ----------------------------------------------------------------
>
> Key: SPARK-7412
> URL: https://issues.apache.org/jira/browse/SPARK-7412
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Major
> Labels: bulk-closed
>
> The Pipelines API (spark.ml package) now includes abstractions for single-label prediction: Predictor, Classifier, Regressor. These assume models are local, where single-Row prediction methods can be used as UDFs. We need to think about how to support distributed models in these abstractions.
> Should the abstractions be modified somehow? Or should there be parallel (or inheriting) abstractions, or a mix-in?
> Motivation: We may start supporting distributed models since linear models, random forests, and other models can get large enough to merit distributed storage and computation.
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