Tuesday, September 10, 2013

Predictive model deployment with PMML

Model deployment used to be a big task. Predictive models, once built, needed to be re-coded into production to be able to score new data. This process was prone to errors and could easily take up to six months. Re-coding of predictive models has no place in the big data era we live in. Since data is changing rapidly, model deployment needs to be instantaneous and error-free.

PMML, the Predictive Model Markup Language, is the standard to represent predictive models. Given that PMML can be produced by all the top commercial and open-source data mining tools (e.g., FICO Model Builder, SAS EM, IBM SPSS, R, KNIME, ...), a predictive model can be easily moved into the production environment once it is represented as a PMML file.

Zementis offers ADAPA for real-time scoring and UPPI for big data scoring which make the entire model deployment process a no-brainer. Given that ADAPA and UPPI are universal PMML consumers (accept any version of PMML produced by any PMML-compliant tool), they can make predictive models instantly available for execution inside the production environment.

Check out the Zementis website for details.

Predictive Models with PMML - Upcoming workshop at UCSD Extension - Oct 24-25

October 24-25, 2013
San Diego Supercomputer Center (SDSC), UC San Diego Campus

The Predictive Model Markup Language (PMML) is the de facto standard to represent data mining and predictive analytic models. With PMML, one can easily share a predictive solution among PMML-compliant applications and systems.
Developed in partnership with the San Diego Supercomputer Center’s (SDSC) Predictive Analytics Center of Excellence (PACE), this 2-day, hands-on workshop, will explore how the PMML language allows for models to be deployed in minutes. You will get to know its business value and the data mining tools and companies supporting PMML. You will also begin to understand the language elements and capabilities and learn how to effectively extract the most out of your PMML code.

Workshop Benefits
  • Practice PMML on SDSC’s Gordon with the guidance of world class instructors from industry and academia.
  • Learn how to represent an entire data mining solution using open-standards
  • Understand how to use PMML effectively as a vehicle for model logging, versioning and deployment
  • Identify and correct issues with PMML code as well as add missing computations to auto-generated PMML code
  • PLUS…Receive a comprehensive tour of SDSC to discover its inner workings, extensive capabilities and current projects.
  • Alex Guazzelli, Ph.D., Vice President of Analytics, Zementis, Inc.
  • Natasha Balac, Ph.D., Director of PACE, SDSC, UC San Diego
  • Paul Rodriguez, Ph.D., Research Programmer Analyst, SDSC, UC San Diego
Scholarships Available!
Thanks to the generous underwriting of Zementis, three (3) half-tuition scholarships are available.
 Learn more and apply
Note: Students should have a fundamental knowledge of data mining methods and basic experience with computer programming language. Students must bring a laptop (MAC or PC) each day to fully participate during the hands-on portion of the workshop.
Course Number: CSE-41184   Credit: 2 units
This course is part of the following Certificate Program(s):

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