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https://issues.apache.org/jira/browse/SPARK-19498?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-19498.
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Resolution: Incomplete
> Discussion: Making MLlib APIs extensible for 3rd party libraries
> ----------------------------------------------------------------
>
> Key: SPARK-19498
> URL: https://issues.apache.org/jira/browse/SPARK-19498
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Affects Versions: 2.2.0
> Reporter: Joseph K. Bradley
> Priority: Critical
> Labels: bulk-closed
>
> Per the recent discussion on the dev list, this JIRA is for discussing how we
> can make MLlib DataFrame-based APIs more extensible, especially for the
> purpose of writing 3rd-party libraries with APIs extended from the MLlib APIs
> (for custom Transformers, Estimators, etc.).
> * For people who have written such libraries, what issues have you run into?
> * What APIs are not public or extensible enough? Do they require changes
> before being made more public?
> * Are APIs for non-Scala languages such as Java and Python friendly or
> extensive enough?
> The easy answer is to make everything public, but that would be terrible of
> course in the long-term. Let's discuss what is needed and how we can present
> stable, sufficient, and easy-to-use APIs for 3rd-party developers.
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