Comments on: Is PMML Useful for Model Deployment? http://latentview.com/blog/2009/01/31/pmml-model-deployment-1/ Practical Analytics for Practical People Thu, 01 Oct 2009 09:38:49 -0700 http://wordpress.org/?v=2.9.2 hourly 1 By: Alex Guazzelli http://latentview.com/blog/2009/01/31/pmml-model-deployment-1/comment-page-1/#comment-8 Alex Guazzelli Fri, 13 Feb 2009 00:26:48 +0000 http://latentview.com/blog/?p=23#comment-8 As pointed out in your blog, PMML is not yet widely used by the predictive analytics community at large, but it is sure gaining ground very fast. In a recent poll at the KDNuggets website, more than 30% of the respondents say that they are using PMML. This means that many scientists are using different tools to build, visualize, and deploy their models. PMML allows for models to be easily moved from one application to another application by overcoming compatibility barriers. Today, the best statistical packages export PMML. These include open-source environments such as R and KNIME. Zementis is now offering the first PMML-based scoring engine as a service. This means that anyone anywhere can implement their models (say, in R or KNIME) and deploy them by using PMML in a matter of minutes (not days or months). Imagine being able to move a model from the scientist's desk to a production environment by just moving PMML files between applications. That's what Zementis' offer is about. On top of it, there is no installation. Since Zementis offers its score engine in the Amazon Elastic cloud, the scoring engine is already installed and ready to be use ... and you pay for only what you use ... less than $1/hour. We believe this paradigm is here to revolutionize the world of predictive analytics since it changes the whole perception of the usability and agility behind predictive models. I have recently created a PMML discussion forum on the AnalyticBridge community (http://www.analyticbridge.com/group/pmml). Feel free to join if you are interested in discussing PMML. Many of the members are part of the DMG (Data Mining Group) which is responsible for shaping the standard itself. So, yes, I couldn't agree more, PMML is the way to go (see http://www.analyticbridge.com/group/pmml). As pointed out in your blog, PMML is not yet widely used by the predictive analytics community at large, but it is sure gaining ground very fast. In a recent poll at the KDNuggets website, more than 30% of the respondents say that they are using PMML. This means that many scientists are using different tools to build, visualize, and deploy their models. PMML allows for models to be easily moved from one application to another application by overcoming compatibility barriers. Today, the best statistical packages export PMML. These include open-source environments such as R and KNIME.

Zementis is now offering the first PMML-based scoring engine as a service. This means that anyone anywhere can implement their models (say, in R or KNIME) and deploy them by using PMML in a matter of minutes (not days or months). Imagine being able to move a model from the scientist’s desk to a production environment by just moving PMML files between applications. That’s what Zementis’ offer is about. On top of it, there is no installation. Since Zementis offers its score engine in the Amazon Elastic cloud, the scoring engine is already installed and ready to be use … and you pay for only what you use … less than $1/hour. We believe this paradigm is here to revolutionize the world of predictive analytics since it changes the whole perception of the usability and agility behind predictive models.

I have recently created a PMML discussion forum on the AnalyticBridge community (http://www.analyticbridge.com/group/pmml). Feel free to join if you are interested in discussing PMML. Many of the members are part of the DMG (Data Mining Group) which is responsible for shaping the standard itself.

So, yes, I couldn’t agree more, PMML is the way to go (see http://www.analyticbridge.com/group/pmml).

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