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Zementis and Hortonworks have partnered to deliver standards-based execution of predictive analytics on a massive, parallel scale.
Ofer Mendelevitch, Director of Data Science of Hortonworks, and Michael Zeller, Founder and CEO of Zementis, present key learnings as to what drives successful implementations of big data analytics projects. Their knowledge comes from working with dozens of companies from small cloud-based start-ups to some of the largest companies in the world.
Hortonworks presents their approach to using Apache Hadoop for predictive models with big data, and the benefits of Hadoop to data scientists. Zementis demonstrates how to quickly deploy, execute, and optimize predictive models from open source machine learning tools like R and Python as well as commercial data mining vendors like IBM, SAP and SAS.
Zementis leverages the PMML open industry standard (Predictive Model Markup Language) providing a higher ROI for Big Data and predictive analytics initiatives. At the same time reducing IT costs, and improving the quality of predictive model management while requiring no change in how data science teams do their day-to-day work.
Whether your company is just beginning to work with predictive analytics or has an experienced data science team this webinar provides valuable insights on how to move predictive models into an operational environment based on Hadoop and Hive and using open industry standards while eliminating the custom coding and delays typically associated with these projects.