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From "Sachin Goel (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1537) GSoC project: Machine learning with Apache Flink
Date Mon, 09 Mar 2015 15:49:38 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1537?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14353134#comment-14353134

Sachin Goel commented on FLINK-1537:

I really like the idea of asynchronous iterations and global state management. I think it
can go a long way while working with batch update methods which can update over the data distributed
over multiple machines, leading to finishing of one pass over data in just a single step.

I have never played with job profiling and memory slash time analysis over distributed systems.
So this should be a really interesting topic to work on.

> 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
>            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
> * 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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