Also, as predictive models become more complex through the use of random forest models, model ensembles, and deep learning neural networks, PMML becomes even more relevant since model recoding is simply not an option.
Zementis has paired up with UCSD Extension to offer the first online PMML course. This is a great opportunity for individuals and companies alike to master PMML so that they can muster their predictive analytics resources around a single standard and in doing so, benefit from all it can offer.
- 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
07/14/14 - 08/25/14
PMML is supported by most commercial and open-source data mining tools. Companies and tools that support PMML include IBM SPSS, SAS, R, SAP KXEN, Zementis, KNIME, RapidMiner, FICO, StatSoft, Angoss, Microstrategy ... The standard itself is very mature and its latest release is version 4.2.
For more details about PMML, please visit the Zementis PMML Resources page.
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