I'm working on designing the API for making a number of back-end machine learning programs available to a streaming data application. One of the many questions that comes up is the format to use for communicating learned models back to the application, which will then execute the models.
It looks like PMML is the most prominent effort to develop a standard for interchange of predictive models. A brief overview is Pechter's 2009 article in SIGKDD Explorations: What's PMML and What's New in PMML 4.0.
I would be curious to hear of any problems or great successes that people have had using PMML for communicating from a learning component to an application, and whether there's any notable competitors to it.
asked Dec 06 '10 at 13:52
PMML is currently supported by all the top commercial and open-source analytic tools. Please see: http://www.dmg.org/products.html
At Zementis, we have been very successful in deploying models expressed in PMML using ADAPA. As a matter of fact, people all over the world have been using ADAPA to deploy and execute models built in a variety of PMML-compliant model building environments. That's because since 2008, in addition to offering ADAPA on site, we also started offering it as a service on the Amazon Cloud. Hourly rates start at $1.
I recently described a situation which demonstrates the power of PMML in an article I wrote for IBM developerWorks entitled "What is PMML?". See: http://www.ibm.com/developerworks/industry/library/ind-PMML1/
Hope this helps answer your question. Also, feel free to post it on the PMML group in LinkedIn (with almost 1,000 members). To join, click: http://www.linkedin.com/groupRegistration?gid=2328634
answered Dec 06 '10 at 17:38