James Taylor First Look - Zementis Update

I spoke to Zementis, back in June of 2011 and got an update on their Universal PMML Plug-in among other things. Since then they report growing client interest with a particular focus on real-time decision-making using real-time scoring in fraud detection for instance. They have also been updating their products. ADAPA, their analytic decision deployment infrastructure, has been expanded and they have just released ADAPA 3.5. This includes support for model ensembles, segmentation, chaining and compositions - multiple models used together as a set to improve predictive power. For instance a set of regression models, one for each node in a decision tree combined with the decision tree itself allow a customer to be put into a useful segment and then scored in a way that is highly predictive for that particular segment. Support for this in ADAPA includes weighted and balanced ensemble models.

Interestingly, support for ensemble models which had previously been added to PMML 4.0 (the Predictive Model Markup Language that Zementis and others use to move predictive analytic models from modeling environment to deployment) is now being extended in PMML 4.1. PMML 4.1 is described here and also adds support for some new model types including score cards and reason codes while further improving pre- and post-processing support.

Zementis' focus on PMML means they have a vendor-neutral approach to modeling -any model from any vendor can be deployed. Today they offer four deployment options - deployment to ADAPA for real-time decision making as a cloud, embedded or server deployment plus their Universal PMML Plug-in for in-database deployment. This last allows PMML models to be pushed into the database as a function and is supported by EMC Greenplum as well as Sybase IQ as partners. In addition a partnership with Datameer allows predictive analytic models to be deployed to Hadoop environments. Zementis hopes and expects to add more deployment options using this plug-in.

Zementis is at the forefront of the one of the main debates about PMML. A typical predictive analytic model involves a large amount of pre-processing - data is transformed from the way it is stored into more analytically meaningful attributes that are then used in the model. ...

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