Friday, June 29, 2012

Synergies and Value Proposition between the R Statistical Package and Zementis ADAPA

The ADAPA Decision Engine provides additional value to all your predictive assets. It is complimentary to R, since it extends your modeling environment into the IT operational domain.

ADAPA® is compatible with R through PMML, the Predictive Model Markup Language, which is the de facto standard to represent predictive models. PMML allows for models to be developed in one application and deployed on another, as long as both are PMML-compliant.

Immediate benefits of using ADAPA

Once a model built in R is saved as a PMML file, it can be directly uploaded in ADAPA. With ADAPA, you can:
  • Execute your models independently of R
  • Overcome memory and speed limitations imposed by R
  • Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
  • Tap into all the advantages of cloud computing with ADAPA on Cloud (IBM SmartCloud or Amazon EC2)
  • Execute your models directly from Excel, by using the ADAPA Add-in for Excel
  • Benefit from using other PMML-compliant model development tools such as KNIME and RapidMiner
  • Deploy your models in minutes, not months (no need for recoding models into production)
  • Manage models via Web Services or a Web console
  • Upload one or many models into ADAPA at once
  • Use rules to implement model segmentation
  • Benefit from the seamless integration of business rules and predictive models through PMML

R PMML support

R offers support for PMML through the R PMML Package available in CRAN. Zementis is a proud contributor to the PMML package which was featured on an article we wrote for The R Journal (to download article, click HERE). The PMML package allows users to export a multitude of predictive models in PMML (for details, click HERE).

We have put together a video which shows how easy it is to export PMML models from R. It uses a simple R script to build a decision tree model using rpart and exports it to PMML using the PMML package. To read posting and watch video, click HERE.

A common industry standard

PMML allows for the de-coupling of two very important modeling phases: development and operational deployment. With PMML, scientists can focus on data analysis and model building using the best of breed model development tools, whereas operational deployment and actual use of the model is made extremely easy and simple with ADAPA.

ADAPA Solutions For

For example, if a data mining scientist develops a decision tree model using R rpart package, all he/she needs to do to effectively deploy his/her model operationally is to save it as a PMML file and uploaded it in ADAPA. Once in ADAPA, the decision tree model is available for all to use, directly by business users and applications. The model may be used by a business user directly from within Excel to score customers for a marketing campaign.

By doing that, PMML allows for the model development environment to be used just for that, model development. Scoring, real-time or batch-mode from anywhere and at anytime, is handled by ADAPA.

Thursday, June 28, 2012

Learn how the IBM / Zementis Partnership simplifies Predictive Analytics

Benefiting from Interoperability
PMML, the Predictive Model Markup Language, has become the de-facto standard to represent not only predictive models, but also data pre- and post-processing. In so doing, it allows for the interchange of models among different tools and environments, avoiding proprietary issues and incompatibilities. 

Model Building: IBM SPSS

IBM SPSS Modeler and IBM SPSS Statistics are extremely powerful data analysis and model building environments. This power is backed-up by their support of PMML. In either tool, predictive models as well as data transformations can be easily exported into PMML. IBM SPSS Statistics, for example, allows for automatic data preparation which can be exported into PMML and subsequently merged into the final PMML file for the entire solution. 

View on-demand replay of the joint IBM SPSS/Zementis webcast focusing on the synergies between IBM SPSS and Zementis ADAPA (presented, May 14th, 2012).  

Model Execution: ADAPA on the IBM SmartCloud

Once exported in PMML, your IBM SPSS models can be readily deployed in the Zementis ADAPA Scoring Engine, where they can be put to work immediately. To minimize total cost of ownership, model execution in ADAPA is now available as a service through the IBM SmartCloud. 

View on-demand replay of the joint IBM/Zementis webcast focusing on predictive analytics deployment and execution on the IBM SmartCloud (presented, May 24th, 2012).

Review IBM developerWorks article about executing predictive solutions using ADAPA on the IBM SmartCloud.

In-database Scoring: UPPI for IBM Netezza 

Predictive solutions expressed in PMML can also be put to work inside the database with the Zementis Universal PMML Plug-in (UPPI) which is now available for IBM Netezza. Since UPPI transforms your complex predictive solutions into SQL functions, these can be readily used in any query and generate instant business decisions and insights where and when you need them.

Thursday, June 7, 2012

IBM/Zementis Webinar: Have You Fully Tapped the Business Value of Predictive Analytics?

The analysis of "Big Data" to support your business objectives is a new to most companies. To extract value and insight from "Big Data", leading organizations increasingly leverage predictive analytics. By using statistical techniques that uncover important patterns present in historical data, companies are able to predict the future. In doing so, they become more precise, consistent and automated in everyday business decisions.

In this webinar, Ed Bottini, IBM’s Global SmartCloud Services Ecosystem Leader,  and Dr. Michael Zeller, Zementis CEO, showcase the technical capabilities of the Zementis ADAPA Decision Engine on the IBM SmartCloud, a solution which combines open standards and cloud computing to reduce complexity and accelerate time-to-market for predictive analytics in any industry and for any business application.

Wednesday, June 6, 2012

Agile Deployment of Predictive Analytics on Hadoop: Faster Insights through Open Standards

Join us for the 2012 Hadoop Summit at the San Jose Convention Center on June 13-14.

Ulrich Rueckert, Data Scientist at Datameer and Michael Zeller, Zementis CEO,  will be presenting on Wednesday, June 13, 1:30-2:10 pm.

Session Abstract:

While Hadoop provides an excellent platform for data aggregation and general analytics, it also can provide the right platform for advanced predictive analytics against vast amounts of data, preferably with low latency and in real-time. This drives the business need for comprehensive solutions that combine the aspects of big data with an agile integration of data mining models. Facilitating this convergence is the Predictive Model Markup Language (PMML), a vendor-independent standard to represent and exchange data mining models that is supported by all major data mining vendors and open source tools (see figure below).

PMML is an XML-based language developed by the Data Mining Group (DMG) which provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications. It provides applications a vendor-independent method of defining models so that proprietary issues and incompatibilities are no longer a barrier to the exchange of models between applications. PMML allows users to develop models within one vendor's application, and use another vendors' applications to visualize, analyze, evaluate or otherwise use the models. Previously, this was very difficult, but with PMML, the exchange of models between compliant applications is now straightforward.

This joint Datameer/Zementis presentation will outline the benefits of the PMML standard as key element of data science best practices and its application in the context of distributed processing. In a live demonstration, we will showcase how Datameer and the Zementis Universal PMML Plug-in take advantage of a highly parallel Hadoop architecture to efficiently derive predictions from very large volumes of data.

Session atendees will learn:
  • How to leverage predictive analytics in the context of big data
  • Introduction to the Predictive Model Markup Language (PMML) open standard for data mining
  • How to reduce cost and complexity of predictive analytics

Welcome to the World of Predictive Analytics!

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