Inside Advanced Scale Challenges|Wednesday, December 13, 2017
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FogHorn Pushes Machine Learning Into Industrial IoT 

As the industrial sector continues to spearhead Internet of Things deployments, investors are targeting analytics and machine learning startups focused on applications like predictive maintenance and streamlining business processes.

Among the latest investees is FogHorn Systems, a developer of "edge intelligence" software for industrial IoT applications, which this week announced the closing of a $30 million funding round led by Intel Capital and Saudi Aramco Energy Ventures.

The IoT startup based in Mountain View, Calif., has so far raised $47.5 million in two funding rounds. The company said it would use the funds to expand its engineering team and marketing efforts. Industry analysts noted that the startup used early funding to incorporate machine learning into its edge analytics framework. It has since moved that capability higher in the technology stack to forge a "fog" computing platform that extends cloud computing to the edge of enterprise networks.

FogHorn recently unveiled a machine learning platform called Lightning ML built on its complex event processing analytics software released last year. The new platform is designed to train and execute machine-learning algorithms used to analyze streaming sensor data. As with other edge technologies, the analytics capability crunches sensor data at its source to speed results.

The scheme generates what the company calls "digital twins" along with other machine learning and AI models at the source of data, eliminating the need to transfer sensor information to the cloud or a datacenter for processing.

FogHorn and its investors claim it is addressing a fundamental computing challenge posed by industrial IoT deployments: producing insights from real-time streaming data generated by sensors without transport and hosting costs, security risks and latency issues.

"Both the promise and challenge of [industrial] IoT [lay] in the ability to convert sensor data to actionable insights that improve customers’ operating efficiency and generate new sources of business value," Jonathan Ballon, general manager of Intel’s (NASDAQ: INTC) IoT Group, noted in a statement. "We believe their edge analytics and machine learning technology will be a critical factor in enabling those operating efficiencies and delivering that business value."

New investor Honeywell Ventures joined Intel Capital and Saudi Aramco along with early investors March Capital Partners, GE Ventures, Dell Technologies Capital, Robert Bosch Venture Capital, Yokogawa Electric Corp., Darling Ventures and seed investor The Hive.

FogHorn partnered earlier this year with Yokogawa Electric to deploy its fog computing approach in industrial IoT settings.

The company claims its machine-learning framework is prompting IoT adopters to reconsider their cloud-first strategies by bringing fog computing closer to sensors on the factory floor.

At least one industry analyst agrees. "Because the edge is becoming a normal part of any IoT conversation and a path to pursue for many vendors, FogHorn is pushing on with machine learning," Ian Hughes of 451 Research noted in an August research report.

"Such capabilities are generally assumed to be high-end supercomputer and cloud functions," he continued. With its focus on machine learning, FogHorn "helps further shift the mindset of its industrial customers from assuming cloud-first for [industrial] IoT."

About the author: George Leopold

George Leopold has written about science and technology for more than 25 years, focusing on electronics and aerospace technology. He previously served as Executive Editor for Electronic Engineering Times.

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