Standards in Predictive Analytics: R, Hadoop and PMML (a white paper by James Taylor)

January 29, 2014

(This article was first published on Predictive Analytics, Big Data, Hadoop, PMML, and kindly contributed to R-bloggers)

James Taylor (@jamet123) is remarkable in capturing the nuances and mood of the data analytics and decision management industry and community. As a celebrated author and an avid writer, James has been writing more and more about the technologies that transform Big Data into real value and insights that can then drive smart business decisions. It is not a surprise then that James has just made available a white paper entitled “Standards in Predictive Analytics” focusing on PMML, the Predictive Model Markup Language, R, and Hadoop.

Why R? 

Well, you can use R for pretty much anything in analytics these days. Besides allowing users to do data discovery, it also provides a myriad of packages for model building and predictive analytics.

Why Hadoop? 

I almost goest without saying. Hadoop is an amazing platform for processing predictive analytic models on top of Big Data.

Why PMML? 

PMML is really the glue between model building (say, R, SAS EM, IBM SPSS, KXEN, KNIME, Python scikit-learn, …. ) and the production system. With PMML, moving a model from the scientist’s desktop to production (say, Hadoop, Cloud, in-database, …) is straightforward. It boils down to this:

R -> PMML -> Hadoop

But, I should stop here and let you read James’ wise words yourself. The white paper is available through the Zementis website. To download it, simply click below.


And, if you would like to check James’ latest writings, make sure to check his website:

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