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From "Till Rohrmann (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1537) GSoC project: Machine learning with Apache Flink
Date Mon, 09 Mar 2015 14:04:38 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1537?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14353005#comment-14353005

Till Rohrmann commented on FLINK-1537:

Hi Sachin,
great to hear that you're interested in working on Flink's machine learning library. Since
the work on the ML library just recently started, there still a lot of create leeway. Depending
on your interests, I'm sure that we can find an appropriate topic. There are problems which
are more related to efficiently implementing algorithms with Flink and others where one has
to work more on the system-side. 

It is very good that you have already gathered some experience with distributed systems and
even better that you also implemented a distributed random forests algorithm. But still, I'd
recommend to familiarise yourself a little bit with the system by reading the [documentation|http://ci.apache.org/projects/flink/flink-docs-master/programming_guide.html],
going through the example jobs contained in the [repository|https://github.com/apache/flink/tree/master/flink-examples]
and maybe even try to implement one job yourself. That is the best way to understand Flink.

Next it would be awesome if you could implement the random forest algorithm with Flink. That
could be your first contribution to the project. That way, the rest of the community will
get to know you and you can see if you have fun being part of the community. Since you already
implemented the job on Hadoop, it should not be too difficult for you to also implement in
Flink. But be aware that Flink offers a richer API than Hadoop and, thus, some things can
be done in a different way.

In the next days, I'll merge the current state of the machine learning library to the master.
You can find the current version in my private [branch|https://github.com/tillrohrmann/flink/tree/flink-ml].
Would be great if you could stick to the paradigm of {{Transformer}} and {{Learner}}.

So just tell me, what if you have a specific topic you'd like 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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