Friday, November 9, 2012

Model Deployment with PMML, the Predictive Model Markup Language

The idea behind this demo is to show you how easy it is to operationally deploy a predictive solution once it is represented in PMML, the Predictive Model Markup Language.

As a model building environment, I use KNIME to generate a neural network model for predicting customer churn. Once data pre-processing and model are represented in PMML, I go on to deploy it in the Amazon Cloud using the ADAPA Scoring Engine and on top of Hadoop using the Universal PMML Plug-in (UPPI) for Datameer. So, the very same model is readily available for execution in two very distinct Big Data platforms: cloud and Hadoop.



The easy of model deployment and interoperability between platforms is the power of PMML, the de facto standard for predictive analytics and data mining models.

Resources:

  1. Download the KNIME workflow used to generate a sample neural network for predicting churn
  2. Download the PMML file created during the demo

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