FogHorn Introduces Drag-and-Drop Analytic Authoring with Latest Lightning Edge AI Platform Release

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According to a new press release, “FogHorn, a leading developer of edge computing software for commercial and industrial Internet of Things (IoT) solutions, today announced the availability of new Lightning™ Edge AI platform features, including tools and enhancements to empower operations technology (OT) professionals. The new drag-and-drop analytic programming capabilities and rich visualization dashboards enable OT staff to derive insights more quickly from real-time data without the need for assistance from data science teams.”

The release goes on, “Industry analysts recognize FogHorn Lightning Edge AI as a leader in IoT-AI platforms for industrial markets. The Lightning edge computing platform brings intelligence to the edge, at or near the point where data originates, and facilitates analysis with the lowest latencies to improve operational outcomes. Artificial intelligence (AI) is enabled through built-in closed-loop edge-to-cloud machine learning, where FogHorn Lightning can detect drifts in model accuracies and automatically trigger cloud-based retraining with Google Cloud Platform (GCP) and now, Microsoft Azure IoT, and republish new models to the edge in an iterative fashion until the expected accuracy is reached.”

It adds, “A visual programming tool, VEL Studio, creates simple to sophisticated analytic expressions that derive actionable insights from streaming control & sensor data. A newly introduced drag-and-drop library of over 100 built-in code blocks lets OT professionals perform traditional data science tasks without the need for any programming skills. This new functionality allows users to simply drag blocks to the workspace, fill in required parameters and connect the code blocks. These code blocks perform analytic functions, including; data cleansing and filtering, data collection and type conversion, event/pattern detection, signal processing and mathematical and statistical analysis. FogHorn also released OT-centric blocks for manufacturing-specific use cases to make it even easier to create advanced analytics including anomaly and failure condition detection.”

Read more at foghorn.io.

Image used under license from Shutterstock.com

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