Apache Big Data Europe 2016

Big Data applications rely on machine learning to derive new value. Model training and deployment are handled by different people in different environments, which makes model transferability a major concern.



This talk inquires into popular R, Scikit-Learn and Apache Spark model types, and connects them at a standardized PMML representation level. PMML adds value to all stages of the workflow, starting from model interpretation, reorganization and persistence, and ending with fully-automated model deployment to schema-full Big Data frameworks.



Attendees will learn that models are not locked-in "black boxes", but easily accessible and programmable components in the application layer. This realization should translate to improved workflows, and smarter and more performant applications.