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Predict! Issue #34

Zementis is proud to announce the availability of a PMML Converter for Python. PMML, the Predictive Model Markup Language, is the de facto standard for predictive analytics. It allows for models to be moved from one platform to another without the need for custom code. PMML is already produced by most commercial and open-source data mining tools including IBM SPSS, KNIME, R, SAS, SAP, … and now also for Python.
PMML usage has grown exponentially with the advent of Big Data and it is no surprise that PMML training is in high demand. Zementis has partnered with UCSD Extension to offer an online PMML course. Registrations are open and classes start now in July. Don’t miss it!

Best Regards,

Michael Zeller

CEO, Zementis Inc.

Introducing Py2PMML
Python to PMML Converter


The Zementis Python to PMML Converter (Py2PMML) provides an easy to use interface to translate your Python-generated machine learning models into PMML, the Predictive Model Markup Language standard. In particular, it allows for models built using scikit-learn to be consumed by the Zementis ADAPA and UPPI scoring engines.
Once translated into PMML, models can be easily deployed and scored against new incoming data. For example, models can be deployed in ADAPA for real-time scoring or UPPI for Big Data scoring, in-database or Hadoop.
How does it work?
Easy! Once you build your model using the scikit-learn library in Python, all you need to to do is write out a text file containing the model parameters. This file is then used by Py2PMML to generate the corresponding PMML file for your model. With the PMML file in hand, you can simply deploy and execute it using one of the Zementis scoring engines in the operational platform of your choice.

Online PMML Course at UCSD Extension 

Starting on July 14, 2014


Zementis has teamed with UCSD Extension to offer an online PMML Course, starting soon on July 14. This online course will explain how the PMML language allows for models to be deployed in minutes. You will get to know its business value as well as the landscape of 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.

The course is divided into six weeks:

  • Week 1: PMML overview
  • Week 2: Language elements and attributes
  • Weeks 3 and 4: Data pre- and post processing
  • Week 5: Model elements
  • Week 6: Multiple models: ensemble, segmentation, chaining.

Students work at their own pace with each week featuring an unique lecture, PMML samples and exercises, and reading material.


Course Benefits

  • 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

Ready to register?

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