Wednesday, October 1, 2014

Zementis/Teradata Whitepaper: Massively Parallel In-database Predictions with PMML


Zementis and Teradata have teamed up to make available to you a whitepaper which not only discusses the benefits of in-database scoring using UPPI for Teradata/Aster but also shares performance numbers that will blow you away! Enjoy!

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Abstract

Open standards enable interoperability and portability across systems and solutions. Such a level of flexibility creates new opportunities for addressing exceedingly demanding business 
agility and performance requirements. The Predictive Model Markup Language (PMML) is the embodiment of an open standard and delivers such benefits in the world of data mining and predictive analytics. This means that models developed in any environment and tool set can be deployed and used in a completely different system. 

In the context of Big Data, the urgent need to apply the power of predictive analytics to derive reliable predictions-and, hence, business decisions-from vast amounts of data collected by 
many organizations is a key requirement. In this paper, we discuss how the PMML standard enables embedding advanced predictive models directly into the database or the data warehouse, alongside the actual data to be scored. More importantly, we show how we can easily take advantage of a highly parallel database architecture to efficiently derive predictions from very large volumes of data.

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