FogHorn, a developer of edge intelligence software for industrial and commercial Internet of Things (IoT) solutions, announced the availability of its Lightning 2.0 software. The additions to the Lightning portfolio establish new industry benchmarks for edge-based machine learning (EdgeML), massively scalable edge deployment support, zero-touch sensor configuration, out-of-the-box multi-cloud integration, and next generation OT (Operational Technology) tools.
FogHorn’s Lightning product portfolio brings a dimension to industrial IoT (IIoT) by embedding edge intelligence locally, as close to the source of streaming sensor data as possible. The FogHorn platform is a highly compact, advanced and feature-rich edge intelligence solution that delivers unprecedented low latency for onsite data processing and real-time analytics in addition to its ML and Artificial Intelligence (AI) capabilities.
“In 2018, we have seen a large volume of initial edge intelligence deployments experience great success across a range of industries and use cases,” says Pierce Owen, principal analyst at ABI Research. “Now, the task at hand is to efficiently deploy these projects at large scale. This latest release from FogHorn focused on going from pilot to broad commercial roll out is well timed with the state of the market’s needs.”
The Lightning 2.0 release extends the company’s leadership across of number of critical edge computing dimensions, including:
Advancements in edge-based Machine Learning
FogHorn was first the first company toto IIoT. With 2.0, these capabilities have been augmented to support Predictive Model Mark Up Language (PMML), enabling any compliant machine learning model to be run at the edge.
The Lightning platform offers “edgification” of machine learning models, off-loading data pre- and post-processing from the main ML model to the VEL CEP engine for optimal performance in the smallest compute footprint.
Finally, FogHorn easily enables the emerging trend of Sensor Fusion with its ability to simultaneously process and infer on multiple data streams, of different types. This, in addition to executing Deep Learning models on constrained edge compute devices has resulted in the rise of new and sophisticated use cases.
Improved automation and scalability
The release adds critical capabilities to improve sensor deployment automation and scalability, including auto sensor-discovery, sensor fusion, edge device auto-registration, and single click deployment to thousands of devices at once. Data publishing integration with leading cloud providers, along with a deep integration withfurther streamline production deployment roll outs, speeding time to value with cloud pre-integration.
Enhanced operator user experience
Along with the enhanced automation capabilities, the Lightning platform has an innovative new user interface (UI), designed specifically for OT team members. Beyond an enhanced look and feel, the new UI shortens the learning curve for new users, and offers an intuitive, simple way to execute a range of common operational activities.
Further, the 2.0 release introduces VEL Studio (beta), a streamlined approach to creating and debugging flow-reactive analytic expressions, delivering significant programming efficiency for the edge, while dramatically reducing the barrier to entry.
“It’s very exciting to see a number of our customers aggressively rolling out the Lightning platform and experiencing significant improvements in performance, automation, yield, quality and cost savings,” said Sastry Malladi, CTO at FogHorn.
“The 2.0 release was designed to help our customers scale quickly and accelerate time-to-value in the most innovative, automated, and user-friendly way. This release further extends our technology and solution leadership in real-time edge intelligence, and we are fortunate to have such passionate customers and ecosystem partners continuing to help us push the envelope of innovation in industrial edge intelligence.”
In 2018, FogHorn has been featured in over 20 analyst reports, including five Gartner reports covering IIoT gateways, data science and machine learning, analytics, edge computing, and the IoT. These reports include the Hype Cycle for the Internet of Things, 2018, Market Guide for Edge Computing Solutions for Industrial IoT, Hype Cycle for Analytics and Business Intelligence, 2018, Hype Cycle for Data Science and Machine Learning, 2018, and Market Guide for Industrial IoT Gateways.