IIoT gets Lightning ML from FogHorn Systems
|Richard Harris in IoT Monday, July 17, 2017|
Machine learning IIoT software update announced for FogHorn's Lightning.
Accenture predicts that IIoT can add $14.2 trillion to the global economy by 2030. However, industrial environments present a challenge to status quo methods for data collection and analysis.
“The money and time required to move massive amounts of machine data to the cloud for analysis, only to send the results back to the edge, often makes little sense,” said Mike Guilfoyle, Director of Research and Senior Analyst at ARC Advisory Group. “In many instances cloud computing won’t be practical, necessary, or desirable. The reality is that edge intelligence is critical to a successful overall analytics strategy."
“FogHorn is accelerating the pace of innovation in edge computing by not just democratizing analytics but by making machine learning accessible to industrial operators,” said FogHorn CEO David C. King. “The addition of FogHorn Lightning ML is a monumental leap forward in delivering on the promise of actionable insights for our IIoT customers. In the initial launch of FogHorn’s Lightning platform, we successfully miniaturized the massive computing capabilities previously available only in the cloud. This allows customers to run powerful big data analytics directly on operations technology (OT) and IIoT devices right at the edge through our complex event processing (CEP) analytics engine. With the introduction of Lightning ML, we now offer customers the game changing combination of real-time streaming analytics and advanced machine learning capabilities powered by our high-performance CEP engine.”
Machine Learning at the Edge
Lightning ML brings the power of machine learning at the edge in three groundbreaking ways:
- Leverages existing models and algorithms: Industrial customers can plug in and execute proprietary algorithms and machine learning models on live data streams produced by their physical assets and industrial control systems.
- Makes machine learning OT-accessible: Non-technical personnel can use their tools to generate machine learning insights without the need to constantly rely on in-house or third party data scientists.
- Runs in tiny software footprint: The ML update enables complex machine learning models to run on highly-constrained compute devices such as PLCs, Raspberry Pi systems, tiny ruggedized IIoT gateways, as well as more powerful Industrial PCs and servers. Even with the addition of advanced machine learning capabilities, the complete Micro edition of the Lightning ML platform requires less than 256MB of memory footprint.
On-Premise Centric and Cloud Agnostic
The ML version of the software platform can run entirely on premise or connect to any private cloud or public cloud environment. This gives customers maximum flexibility in selecting the best deployment model in terms of IT infrastructure, security policy and cost.
Designed for Operational Technology
Lightning ML has been specifically designed to empower OT users through a simple drag-and-drop authoring tool that abstracts away the complexities of an underlying IIoT deployment, allowing operators to focus on translating their domain expertise into meaningful analytics and machine learning insights.
“OT staff are domain experts in their respective industrial environments, but not necessarily experts in edge computing and advanced IT,” said FogHorn CTO Sastry Malladi. “By giving them intuitive tools to automate, monitor and take action on their industrial data in real-time, operators can enhance situational awareness, prevent process failures and identify new efficiencies that lead to huge business benefits. This is a very different approach from other IT-centric solutions that fail to leverage the tribal knowledge of key OT experts.”
Read more: http://www.foghorn.io/
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