David C. King,CEOIn the modern era, there’s action out at the “edge” across the manufacturing, smart city, smart building, energy grid, oil rig, and automobile realms. The advent of the Industrial Internet of Things (IIoT) and the need for rapid data processing have created the need for a new computing paradigm, requiring edge computing in industrial and commercial applications.
“Instead of moving massive amounts of data from edge devices to the cloud, deep compute capabilities can be deployed in the field, getting rid of the traditional data processing layer while also removing the latency and costs of transmitting data and getting rid of the data processing layer,” says David C. King, CEO, FogHorn Systems.
For new-age industries to increase productivity, gain actionable insights from data lakes, and optimize existing processes, an ecosystem where ‘‘minds and machines’ work hand-in-hand is imperative. Headquartered in Mountain View, CA, FogHorn’s “edge intelligence” platform, Lightning ML, brings the power of real-time computing, artificial intelligence (AI), big data analytics and machine learning (ML) to edge devices— driving new levels of performance and productivity.
According to King, irrespective of the type of operations, be it advanced monitoring and diagnostics, asset performance optimization, operational intelligence or predictive maintenance, FogHorn’s robust edge intelligence platform enables businesses to achieve unprecedented levels of automation and operational efficiency.
“Our newest version of the machine learning platform, Lightning ML, is a monumental leap forward in delivering on the promise of actionable insights for our IIoT customers,” asserts King. The firm has successfully miniaturized the massive complex event processing (CEP) analytics capabilities that were previously available only in the cloud. The CEP serves as the core to its platform, enabling users to unlock critical insights from the data generated by edge devices through advanced analytics and machine learning.
The CEP analytics engine empowers customers to perform seamless big data analytics directly on operations technology and IIoT devices in real-time, shifting the processing burdens away from distant cloud servers.
Lightning ML is a monumental leap forward in delivering on the promise of actionable insights for our IIoT customers
Industrial users can also gain higher bandwidth and tremendous saving in hosting costs, security advantages, and insights that add real-time value to their business operations.
FogHorn is accelerating the pace of innovation in edge computing by not just democratizing analytics, but by making machine learning accessible to industrial operators.
“This is important because data becomes stale if not analyzed immediately. The valuable information regarding any anomalies needs to be acted upon by operators in real time,” says King.
Edge computing, powered by ML algorithms, helps organizations to enhance situational awareness, prevent process failures, and identify new efficiencies that lead to improved business outcomes.
FogHorn differentiates from its competitors by offering an intelligence platform that is built “edge-up”, and thereby easily fits into traditional technology stacks of industrial sectors. This eliminates the need for lengthy and expensive deployments. Additionally, the firm’s software is ideally suited for OEMs, systems integrators and end customers catering to several industries such as manufacturing, power and water, oil and gas, mining, transportation, healthcare, retail, as well as smart grid, smart city and smart car applications.
FogHorn firmly believes that edge computing is necessary and critical to enable IIoT transformation. So it continues to add more capabilities and enhancements to its existing platform, while creating value-added edge applications in the industrial edge computing space. With the best-in-class edge intelligence platform, FogHorn stays true to its philosophy of connecting human intelligence and machine data to accelerate the digital industrial transformation underway.