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From "Till Rohrmann (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2050) Add pipelining mechanism for chainable transformers and estimators
Date Wed, 20 May 2015 08:44:59 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2050?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14552007#comment-14552007

Till Rohrmann commented on FLINK-2050:

At the moment, I would say keep the model and algorithm coupled. But if the latter approach
turns out to be more suitable we can easily switch the architecture.

> Add pipelining mechanism for chainable transformers and estimators
> ------------------------------------------------------------------
>                 Key: FLINK-2050
>                 URL: https://issues.apache.org/jira/browse/FLINK-2050
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Till Rohrmann
>              Labels: ML
>             Fix For: 0.9
> The key concept of an easy to use ML library is the quick and simple construction of
data analysis pipelines. Scikit-learn's approach to define transformers and estimators seems
to be a really good solution to this problem. I propose to follow a similar path, because
it makes FlinkML flexible in terms of code reuse as well as easy for people coming from Scikit-learn
to use the FlinkML.

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