Predictive Analytics Deployment to Mainframe or Hadoop

Learn how to achieve a standards-based, efficient approach to analytics deployment from open source like R and commercial analytics products to a wide range of platforms including mainframes like IBM zSystems and new data infrastructure like Hadoop.

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The big challenge for analytics-driven organizations today is closing the gap between deriving an analytic result and getting the ROI. Organizations need a consistent and efficient way to deploy analytic results into everything from systems of record like mainframes to modern big data infrastructure.

Join James Taylor, CEO of Decision Management Solutions and Michael Zeller, CEO of Zementis, in this live webinar to learn how the Predictive Model Markup Language (PMML) provides an XML standard that streamlines the deployment of predictive analytic models. With PMML a model can be developed in one tool or language, whether open source like R or commercial predictive analytics products, and easily migrated to a wide range of operational systems including mainframes like IBM zSystems and new data infrastructure like Hadoop with Spark / Storm / Hive.

You will learn:
• The challenges of an increasingly complex analytic environment.
• How analytics increase the value of legacy systems of record, mainframes and big data infrastructure.
• Why PMML is the critical glue between heterogeneous analytics environments.

The presenters will use case studies to outline this proven, standard-based approach to analytics deployment in today’s complex predictive analytics environments.

Free White Paper

Register for the new paper by James Taylor, Standards-based Deployment of Predictive Analytics – Using a standards-based approach to deploy predictive analytics on operational systems from mainframes to Hadoop.

 


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