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https://issues.apache.org/jira/browse/FLINK-1537?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Robert Metzger updated FLINK-1537:
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    Component/s: Machine Learning Library

> GSoC project: Machine learning with Apache Flink
> ------------------------------------------------
>
>                 Key: FLINK-1537
>                 URL: https://issues.apache.org/jira/browse/FLINK-1537
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Priority: Minor
>              Labels: gsoc2015, java, machine_learning, scala
>
> Currently, the Flink community is setting up the infrastructure for a machine 
> learning library for Flink. The goal is to provide a set of highly optimized 
> ML algorithms and to offer a high level linear algebra abstraction to easily 
> do data pre- and post-processing. By defining a set of commonly used data 
> structures on which the algorithms work it will be possible to define complex 
> processing pipelines. 
> The Mahout DSL constitutes a good fit to be used as the linear algebra 
> language in Flink. It has to be evaluated which means have to be provided to 
> allow an easy transition between the high level abstraction and the optimized 
> algorithms.
> The machine learning library offers multiple starting points for a GSoC 
> project. Amongst others, the following projects are conceivable.
> * Extension of Flink's machine learning library by additional ML algorithms
> ** Stochastic gradient descent
> ** Distributed dual coordinate ascent
> ** SVM
> ** Gaussian mixture EM
> ** DecisionTrees
> ** ...
> * Integration of Flink with the Mahout DSL to support a high level linear 
> algebra abstraction
> * Integration of H2O with Flink to benefit from H2O's sophisticated machine 
> learning algorithms
> * Implementation of a parameter server like distributed global state storage 
> facility for Flink. This also includes the extension of Flink to support 
> asynchronous iterations and update messages.
> Own ideas for a possible contribution on the field of the machine learning 
> library are highly welcome.



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