Predictive Model Markup Language: PMML

Data Mining Group (DMG): an independent, vendor led consortium
“The Data Mining Group (DMG) is an independent, vendor led consortium that develops data mining standards, such as the Predictive Model Markup Language (PMML).
PMML is the leading standard for statistical and data mining models and supported by over 20 vendors and organizations. With PMML, it is easy to develop a model on one system using one application and deploy the model on another system using another application.”
http://www.dmg.org/
Real-time intruder detection with R, PMML, and WSO2 CEP
“This tutorial will explain how you could use WSO2 Complex Event Processor (CEP) and Predictive Model Markup Language (PMML) for building real-time predictive modeling applications, using network intruder detection as an example. At the end of this tutorial, you will be able to create and test PMML models in the CEP for practical applications.”
http://wso2.com/library/articles/2014/11/article-real-time-intruder-detection-with-r-pmml-and-wso2-cep/

Predictive Model Markup Language (PMML)

Predictive Model Markup Language
“The Predictive Model Markup Language (PMML) is an XML-based file format developed by the Data Mining Group to provide a way for applications to describe and exchange models produced by data mining and machine learning algorithms. It supports common models such as logistic regression and feedforward neural networks.
Since PMML is an XML-based standard, the specification comes in the form of an XML schema.”
http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language
What is PMML?
“PMML stands for “Predictive Model Markup Language”. It is the de facto standard to represent predictive solutions. A PMML file may contain a myriad of data transformations (pre- and post-processing) as well as one or more predictive models. Because it is a standard, PMML allows for different statistical and data mining tools to speak the same language. In this way, a predictive solution can be easily moved among different tools and applications without the need for custom coding. For example, it may be developed in one application and directly deployed on another. Traditionally, the deployment of a predictive solution could take months, since after building it, the data scientist team had to write a document describing the entire solution. This document was then passed to the IT engineering team, which would then recode it into the production environment to make the solution operational. With PMML, that double effort is no longer required since the predictive solution as a whole (data transformations + predictive model) is simply represented as a PMML file which is then used as is for production deployment. What took months before, now takes hours or minutes with PMML. PMML is developed by the Data Mining Group (DMG), a consortium of commercial and open-source data mining companies. The latest version of PMML, version 4.1, was released by the DMG in December 2011. Since PMML is XML-based, it is not rocket science. Its structure follows a set of pre-defined elements and attributes which reflect the inner structure of a predictive workflow: data manipulations followed by one or more predictive models.”
http://www.kdnuggets.com/faq/pmml.html

How Predictive Model Markup Language Puts Big Data to Work Faster for Business
http://data-informed.com/pmml-puts-big-data-to-work/

 

machine learning data set repository
http://mldata.org/