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Machine Learning at the Edge is the Future of the IoT

By Ken Briodagh April 20, 2015

Data is a bit like Midas’ gold. You want it, but the price is pretty heavy. The cost of mining all that data gold from your IoT sensors is that it’s meaningless and valueless until you interpret it and organize it into actionable information.

One method for taking that process out of the manual has been clever application of a variety of algorithms to divide up the incoming data and place it in appropriate buckets for review. But why stop there? That’s the question asked by Jamie MacLennan, CTO at predictive Big Data analytics firm Predixion. He says the answer isn’t in a simple algorithm. The answer, he says, is in his Machine Learning Semantic Model (MLSM).

“The convolutions you have to do to get the data to work for an algorithm is typically beyond human skill,” he said. “The MLSM allows you to work with broad categories of data points that will show you trends and help you know how to take action by doing the work of fitting individual datum into the categories.”

This, he said, is known as working with the schema rather than the data: a more helpful level of information knowledge for humans. A basic schema for use in the IoT, for example might include: device id, session id, sensor value and time stamp. So, as the raw data comes in, the MLSM would determine into which of those four categories each bit of data should go, allowing the human reviewer to more efficiently see patterns and make decisions.

Because this happens automatically, not in the cloud, but at the edge, right where the data is needed and can be acted upon quickly, MLSM becomes ideal for IoT.

“MLSM can be deployed in any modality,” he said. “There isn’t an IoT scenario where it’s not usable but it’s best for predictive maintenance like for medical equipment, infrastructure or supply chain.”

Maybe, thanks to MLSM, data is better than Midas’ gold. At least you can still enjoy your breakfast. 




Edited by Dominick Sorrentino
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