Zementis: Teradata and IBM Partnership, Hadoop/ML best practices, James Taylor on standards

Zementis Universal PMML Plug-in (UPPI), which enables the execution of standards-based predictive analytics, now works for Teradata and IBM PureData systems. Also learn about Best practices for Hadoop and Machine Learning and read James Taylor on analytics standards.

Zementis Zementis had a great 2013 and is starting the new year several developments that are of interest to KDnuggets readers:

Datameer / Zementis webinar January 16: Best practices for Hadoop and Machine Learning

Learn best practices for
  • Selecting the right Machine Learning approach for business and IT
  • Visualizing machine learning on Hadoop, and
  • Leveraging existing algorithms on Hadoop
Zementis and Teradata announce partnership
Zementis and Teradata Corporation announced the availability of the Universal PMML Plug-in, a scalable in-database scoring solution based on the Predictive Model Markup Language (PMML) industry standard, for Teradata analytic platforms.

The Zementis Universal PMML Plug-in (UPPI) enables the execution of standards-based predictive analytics directly within the Teradata Unified Data Architecture™. Users can now easily deploy predictive models built in R, IBM SPSS, SAS Enterprise Miner and other popular analytic tools on Aster and/or Teradata to achieve scale.

Zementis and IBM PureData Systems deliver Big Data insights through predictive analytics

UPPI is also available for IBM PureData Systems for Hadoop and IBM InfoSphere BigInsights. In this way, it leverages the inherent processing power of Hadoop/Hive and IBM's big data platform to deliver insights and value at the speed of business.

Standards in Predictive Analytics: PMML, by James Taylor

PMML offers an open, standards-based approach to operationalizing predictive analytics. This is a critical need for organizations looking to maximize the value of predictive analytics: unless predictive analytic models can be effectively operationalized, injected into operational systems, then the danger is that they will sit on the shelf and add no value.

Similarly if it takes too long to operationalize them-weeks or months say-then the value of the model will degrade even as the cost to implement the model rises. The wide range of deployment options for PMML models addresses these concerns and also means that organizations can relax their concerns about multiple analytic development environments. If models can be generated as PMML and that PMML can be executed widely then it is possible to create an environment in which models are developed with any analytic tool and run anywhere.