He also updated a FAQ about predictive analytics and data mining that you can here

The essential idea of PMML is that a predictive model, and more generally a statistical or data mining model, should not be thought of as code, but rather abstracted and described as metadata about the underlying data that it models. The PMML standard specifies an XML format for this metatadata.

The reason that this point of view is important is so that one application (the model producer) can produce the model, while another application (the model consumer) can use the model for scoring data. The model consumer can be integrated into production and operational systems and models can then be updated simply by reading new PMML files.

With this approach predictive models in operational systems can be updated quickly and easily. In contrast, when predictive models are viewed as code and new code is added to operational systems, a careful QA process is required before any new code can be deployed.

The upcoming version of PMML (PMML version 4.1, which should be released in the Fall of 2011) supports multiple models. This PMML feature was championed by Open Data Group over the past few years based upon its experience building predictive models over big data.

As the amount of data increases, building predictive models using multiple models (segmented models, hierarchical models, and related techniques) is absolutely critical. For big data, there is really no alternative.

I expect that with the explosion of big data and big data analytics, and with PMML’s support for multiple models, that PMML will begin to be an essential component of any analytic infrastructure that supports big data.