In-Stream Analytics, ADAPTIVE Machine Learning
Machine Learning today tends to be “open-loop” – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called “In-Stream Analytics (ISA)”.
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Explicit definition of LEARNING: If Learning is the process of “generalization from experience”, we can be more explicit and say that“generalization from past experience AND results of new action” is the true definition of Learning!
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Adaptive Machine Learning (AML) methods are necessary for closed-loop solutions. Off-line or batch methods are suitable for investigations and explorations but the solution to any business problem will require it to be closed-loop with the time available between event and action varying from milliseconds to hours, days and months. The adaptive nature of AML, where the solution adapts to the changing environment, renders the solution fully automatic removing the need for human intervention in the best case.
The need to close the loop is unavoidable for any sustaining ML business application! Here is the conceptual architecture for a PRACTICAL Adaptive Machine Learning system:
FULL discussion at :
An earlier post by Bill Vorhies, “Stream Processing and Streaming Analytics – How It Works“, is complementary with more specific details on the data engineering aspects of In-Stream Analytics.
About the Author:
Dr PG Madhavan is the Founder of Syzen Analytics, Inc. He developed his expertise in Analytics as an EECS Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft Architect and startup CEO. PG has been involved in four startups with two as Founder.