R4ML: A Scalable R for Machine Learning

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useR!2017: R4ML: A Scalable R for Machine Learning

Keywords: R, Distributed/Scalable, Machine Learning, SparkR, SystemML
Webpages: https://github.com/SparkTC/R4ML
R is the de facto standard for statistics and analysis. In this talk, we introduce R4ML, a new open-source R package for scalable machine learning from IBM. R4ML provides a bridge between R, Apache SystemML and SparkR, allowing R scripts to invoke custom algorithms developed in SystemML's R-like domain specific language. This capability also provides a bridge to the algorithm scripts that ship with Apache SystemML, effectively adding a new library of prebuilt scalable algorithms for R on Apache Spark. R4ML integrates seamlessly SparkR, so data scientists can use the best features of SparkR and SystemML together in the same script. In addition, the R4ML package provides a number of useful new scalable R functions that simplify common data cleaning and statistical analysis tasks.
Our talk will begin with an overview of the R4ML package, its API, supported canned algorithms, and the integration to Spark and SystemML. We will walk through a small example of creating a custom algorithm and a demo of canned algorithm. We will share our experiences using R4ML technology with IBM clients. The talk will conclude with pointers to how the audience can try out R4ML and discuss potential areas of community collaboration.





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