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From "Peter Knight (JIRA)" <j...@apache.org>
Subject [jira] [Issue Comment Deleted] (SPARK-19498) Discussion: Making MLlib APIs extensible for 3rd party libraries
Date Fri, 05 Oct 2018 10:03:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-19498?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Peter Knight updated SPARK-19498:
---------------------------------
    Comment: was deleted

(was: Thank you for your e-mail. I am on holiday until Wednesday 25th July.

Many thanks,

Pete

Dr Peter Knight
Sr Staff Analytics Engineer| UK Data Science
GE Aviation
AutoExtReply
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> 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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