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Posted to issues@spark.apache.org by "Joseph K. Bradley (JIRA)" <ji...@apache.org> on 2018/06/13 22:58:00 UTC
[jira] [Updated] (SPARK-19498) Discussion: Making MLlib APIs
extensible for 3rd party libraries
[ https://issues.apache.org/jira/browse/SPARK-19498?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Joseph K. Bradley updated SPARK-19498:
--------------------------------------
Shepherd: (was: Joseph K. Bradley)
> 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
>
> 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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