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https://issues.apache.org/jira/browse/FLINK-2056?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14556132#comment-14556132
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ASF GitHub Bot commented on FLINK-2056:
---------------------------------------

GitHub user thvasilo opened a pull request:

    https://github.com/apache/flink/pull/714

    [FLINK-2056] [ml] [docs]  Add guide to create a chainable predictor in docs

    Adds documentation explaining chaining in FlinkML, mostly aimed at 
developers.

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/thvasilo/flink pipeline-docs

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/flink/pull/714.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #714
    
----
commit 7fd6b95af7da399ef332d992bd304adeecf0bb18
Author: Theodore Vasiloudis <[email protected]>
Date:   2015-05-20T16:39:23Z

    Added first version of docs for ML Pipelines

commit 7ef29565b352c3fd24591c225e6c6386cc87e440
Author: Theodore Vasiloudis <[email protected]>
Date:   2015-05-21T14:17:49Z

    More info on types and safety, other error correction

commit ba143ddd7c4e13fec65b8e0b0ee4d445cc5a6ee0
Author: Theodore Vasiloudis <[email protected]>
Date:   2015-05-22T13:21:18Z

    Added link in index

----


> Add guide to create a chainable predictor in docs
> -------------------------------------------------
>
>                 Key: FLINK-2056
>                 URL: https://issues.apache.org/jira/browse/FLINK-2056
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Machine Learning Library
>            Reporter: Theodore Vasiloudis
>            Assignee: Theodore Vasiloudis
>            Priority: Minor
>             Fix For: 0.9
>
>
> The upcoming API for pipelines should have good documentation to guide and 
> encourage the implementation of more algorithms.
> For this task we will create a guide that shows how the pipeline mechanism 
> works through Scala implicits, and a full guide to implementing a chainable 
> predictor, using Generalized Linear Models as an example.



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