FogHorn and Uptake to Deliver Edge-to-Cloud Solution for Industrial Businesses (IoT)

FogHorn and Uptake to Deliver Edge-to-Cloud Solution for Industrial Businesses (IoT)

FogHorn has teamed up with Uptake to bring Edge-to-Cloud Solutions for Industrial Businesses

Chicago headquartered Uptake is the industrial artificial intelligence and IoT software leader for global industrial businesses. It combines data analytics and machine learning (ML) to harness data insights for the global industrial sector.

Uptake logo

FogHorn is the developer of edge intelligence software for industrial and commercial IoT application solutions.

The partnership between ForHorn and Uptake will help streamline the deployment and accelerate time-to-value, of industrial IoT applications. The plans are to integrate FogHorn’s Lightning edge analytics and machine learning platform with Uptake’s purpose-built industrial AI and IoT platform, according to the announcement.

VP of partnerships at Uptake, Abhi Kunté, said, “FogHorn has made tremendous advances in IIoT edge computing over the last few years and has clearly demonstrated the value of bringing analytics and complex event processing close to the source. With this partnership, we’ll deliver more complete edge-to-cloud analytics solutions to our mutual customers across the industrial sector, enabling new solutions for asset performance management that empower users to quickly act on data-driven predictions and recommendations. The result is outcomes that help businesses unlock new value across the enterprise through improved operational performance, including uptime, reliability, and output.”

The rise of connected devices has started demanding new edge architectures. The way we process the data is no longer applicable due to different factors. These different factors are volume, velocity, and variety of data continuously generated by different sensors or connected devices. These three attributes of data vary depending on the different industries.

Due to a large number of connected devices and their intercommunication requirements, a true decentralized and distributed architecture is on the rise. These architectures need to process the data locally to make quick and real-time decisions. This requirement has given birth to Edge computing paradigm. Moving computing power local to edge devices opens up many possibilities and brings in lots of advantages. E.g. Since data processing from the sensors is done at the edge location, there would not be any round-trip latency which would be the case with the current cloud computing model. It is not just IoT but the artificial intelligence and machine learning that are driving the demand for edge computing architecture. Machine learning models can be trained in the cloud and deployed in real-time. The primary objective of bringing processing capabilities of the cloud to the edge devices is reducing the latency in decision making.
Read more Internet of Things demand Edge And Fog Computing models
Lightening portfolio by ForHorn embeds edge intelligence locally and close to the source of streaming sensor data.
FogHorn Logo

According to the announcement, FogHorn’s real-time streaming analytics along with Uptake's Asset Strategy Library and their rich set of machine learning (ML) models with a comprehensive database of industrial content including equipment types, failure mechanisms, and maintenance tasks helps generate reports with meaningful insights into sensor data.

The excerpts from the announcement state that the extension of Uptake’s machine learning and AI models to the edge, pre/post processing the data through FogHorn’s Complex Event Processor (CEP) engine reduces the model size by up to 80%. This enables them to work faster and in significantly smaller compute footprints.

Jointly performing closed-loop edge-to-cloud machine learning and AI. This entails sending edge inferencing to Uptake in the cloud to tune the models, and Uptake sending the updated model back to the edge in an iterative way that’s ideal for changing operational conditions and specifications common in industrial environments.

The promise of a true sensor-to-customer integrated product offering focused on delivering financially-optimized outcomes. This is driven in part by Uptake’s recent release of its Asset Performance Management product portfolio, which provides a holistic view of the entire asset environment, and equips the industrial workforce with the tools they need to make smarter decisions, faster. The result is outcomes based on improved operational performance including uptime, reliability, and output.

Chief Technology Officer (CTO) at FogHorn, Sastry Malladi said,  “Uptake’s cloud-based industrial AI and IoT platform has cracked the code of turning complex data into real-world outcomes, empowering people, machines and businesses to make more informed decisions. By working together, we can combine this critical competency with our edge-based advanced analytics and machine learning, and extend it to the on-premises industrial environment to enable a new class of applications for advanced monitoring and diagnostics, machine performance optimization, proactive maintenance, and operational intelligence use cases.”


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Mandar is a seasoned software professional for more than a decade. He is Cloud, AI, IoT, Blockchain and Fintech enthusiast. He writes to benefit others from his experiences. His overall goal is to help people learn about the Cloud, AI, IoT, Blockchain and Fintech and the effects they will have economically and socially in the future.

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