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How to Apply Machine Learning (R, Apache Spark, H2O.ai) To Real Time Streaming Analytics

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Published on Sep 21, 2016

This video shows how business analysts, data scientists and developers work together to bring an analytic machine learning model into a (real time) production deployment.

The beginning explains in two minutes the methodology before a 10min live demo discusses use cases such as customer churn and predictive analytics to demonstrate how different tooling for visual analytics / data discovery (TIBCO Spotfire), advanced analytics / machine learning (TIBCO Spotfire in conjunction with R, H2O.ai, Apache Spark) and stream processing / streaming analytics (TIBCO StreamBase, TIBCO Live Datamart) are combined by leveraging the same analytic model (e.g. clustering, random forest) without redevelopment.




You are just beginning your journey with deploying analytic models to real time processing? Feel free to contact me to discuss your architecture, challenges and questions… If you want to discover some components by yourself, please check out our new and growing TIBCO Community Wiki (https://community.tibco.com/wiki). It already contains a lot of information about the discussed components, e.g. the page “Machine Learning in TIBCO Spotfire and TIBCO Streambase” (https://community.tibco.com/wiki/mach...). You can also ask questions in the Answers section to get a response by a TIBCO expert or other community members (https://community.tibco.com/answers).

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