- Deep Learning: TensorFlow Programming via XML and PMML - Jun 9, 2017.
In this approach, problem dataset and its Neural network are specified in a PMML like XML file. Then it is used to populate the TensorFlow graph, which, in turn run to get the results.
- The Evolution of IoT Edge Analytics: Strategies of Leading Players - Sep 2, 2016.
This article explores the significance and evolution of IoT edge analytics. Since the author believes that hardware capabilities will converge for large vendors, IoT analytics will be the key differentiator.
- The Data Mining Group releases PMML v4.3 - Aug 2, 2016.
PMML is an application and system independent format for statistical and data mining models. Key PMML 4.3 features include Improved support for post-processing, model types, and model elements, and new models for Gaussian Process and Bayesian Networks. Check PMML session at KDD-16.
- Predictive Analytics Deployment to Mainframe or Hadoop – Webinar, March 3 - Feb 16, 2016.
Join Decision Management guru James Taylor and Michael Zeller, CEO of Zementis, to learn how the Predictive Model Markup Language (PMML) provides a standards-based, repeatable and efficient deployment approach.
- Portable Format for Analytics: moving models to production - Jan 5, 2016.
There are many ways to compute the best solution to a problem, but not all of them can be put into production. The Portable Format for Analytics (PFA) provides a way of formalizing and moving models.
- Zementis – Cool Vendor in Data Science, 2014 - May 22, 2014.
Zementis, named by Gartner a Cool Vendor in Data Science, provides a scalable, standards-based platform to rapidly deploy and execute predictive models.
- The Data Mining Group releases PMML v4.2 Predictive Modeling Standard - Feb 25, 2014.
The Data Mining Group, a vendor-led consortium of companies and organizations developing standards for statistical and data mining models, announced the general availability of version 4.2 of the Predictive Model Markup Language (PMML).