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https://issues.apache.org/jira/browse/IGNITE-1251?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nikita Ivanov updated IGNITE-1251:
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Description:
Essentially, we want to make Ignite as friendly to ML application as possible.
The first step here is to develop a set of basic (distributed) data structures
that can be used in implementing ML algorithms.
We should borrow most of the ideas from the great Apache Spark project:
https://spark.apache.org/docs/latest/mllib-data-types.html Our implementation
should be based on Ignite data grid / compute grid capabilities (instead of
Spark RDD concept).
Implementation language should be Java (as well as making sure that these Java
APIs can be used relatively pain-free from other JVM-based languages such as
Scala and Groovy).
This has also been submitted to:
http://eecs.oregonstate.edu/capstone/submission/?page=allproposals
was:
Essentially, we want to make Ignite as friendly to ML application as possible.
The first step here is to develop a set of basic (distributed) data structures
that can be used in implementing ML algorithms.
We should borrow most of the ideas from the great Apache Spark project:
https://spark.apache.org/docs/latest/mllib-data-types.html Our implementation
should be based on Ignite data grid / compute grid capabilities (instead of
Spark RDD concept).
Implementation language should be Java (as well as making sure that these Java
APIs can be used relatively pain-free from other JVM-based languages such as
Scala and Groovy).
> Develop a library of distributed data types for ML applications.
> ----------------------------------------------------------------
>
> Key: IGNITE-1251
> URL: https://issues.apache.org/jira/browse/IGNITE-1251
> Project: Ignite
> Issue Type: New Feature
> Components: data structures
> Reporter: Nikita Ivanov
> Assignee: Nikita Ivanov
>
> Essentially, we want to make Ignite as friendly to ML application as
> possible. The first step here is to develop a set of basic (distributed) data
> structures that can be used in implementing ML algorithms.
> We should borrow most of the ideas from the great Apache Spark project:
> https://spark.apache.org/docs/latest/mllib-data-types.html Our implementation
> should be based on Ignite data grid / compute grid capabilities (instead of
> Spark RDD concept).
> Implementation language should be Java (as well as making sure that these
> Java APIs can be used relatively pain-free from other JVM-based languages
> such as Scala and Groovy).
> This has also been submitted to:
> http://eecs.oregonstate.edu/capstone/submission/?page=allproposals
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