April 20, 2024

OctaiPipe raises £3.5m in pre-Series A funding for edge AI platform

Today, OctaiPipe, the end-to-end Edge AI platform for industrial IoT, has announced raising £3 million in pre-Series A funding and a £500,000 grant from Innovate UK.

Launched in 2022, OctaiPipe offers data scientists and AI engineers working on critical infrastructure a reliable end-to-end federated learning operations (FL-Ops) platform.

The OctaiPipe platform enables users to rapidly deploy and automate AI to the edge and orchestrate and manage distributed machine learning across scalable networks of smart IoT devices.

I spoke with Eric Topham, CEO and co-founder of OctaiPipe, to learn more.

What is federated learning and why is it so crucial for IoT?

First, let’s do a quick review of edge computing.

Edge computing positions intelligence and processing capabilities closer to where data originates, improving the ability to perform real-time analytics for actionable insights.

As in scenarios such as harsh environments, reducing the amount of data sent to the cloud and between sensors minimizes latency and reduces time, energy, and bandwidth expenditures.

Over the past decade, cutting-edge computing devices have improved to enable more complex learning on a single device. But as Topham explained,

“Systems, particularly automation systems, have a very high sampling rate. When applied across multiple devices, this results in very high volumes of data. The distributions of that data tend to be heterogeneous. Often, signals and behaviors you are trying to learn, as well as anomalies or failures, are rare.

“Therefore, when you try to compute a model on a single edge instance, you get relatively poor performance because you have a low number of relevant events or signals to learn from.”

Traditional edge computing moves this data to a central data warehouse. Once the model parameter set is reached for whatever task you are learning, the data is streamed (usually suboptimally) continuously to the cloud for inference, or the model is sent back to the edge.

However, those models need to be updated quite frequently, meaning that in the current paradigm, more data is often sent to the cloud, paying for network usage and storage.

Furthermore, the scale required does not exist within a single entity.

Therefore, you end up with use cases where the required scale is limited by barriers to data sharing, such as privacy, intellectual property and ownership, such as data privacy, data security and rights. of data access, especially in the case of mission-critical infrastructure. such as public services, defense and telecommunications.

Topham explained:

“You need performance. To get performance, you need scale. Traditional edge computing doesn’t give you the scale. The alternative, which is moving data to the cloud, runs into risk and cost issues at scale. And that’s why.” “I have latent value that just hasn’t been realized.”

OctaiPipe Federated Learning is a new decentralized approach to training AI models that does not require data exchange between IoT devices and cloud servers. In Federated Learning, data from IoT devices is used to train the AI ​​model locally at the Edge, maximizing system performance and resilience, increasing data security, and radically reducing data costs in the cloud.

As Topham shared:

“The world depends on critical infrastructure not to fail but, more than that, to continually improve performance, remain secure, and become increasingly efficient and sustainable. It is clear that AI has the potential to unlock massive gains in critical infrastructure , but only if you can trust that your critical data is safe.”

With OctaiPipe, data scientists working in industries such as energy, utilities, telecommunications, and security can, for the first time, use a secure end-to-end platform to design, deploy, and manage locally federated learning across networks of Edge devices now. scale.

Available as a Microsoft Azure Platform as a Service (PaaS), AWS, or private cloud, the OctaiPipe platform is currently in deployment with more than 20 customers and device original equipment manufacturers (OEMs).

SuperSeed led pre-Series A round with Forward PartnersAlso participating are Arm-backed D2, Atlas Venture, Martlet Capital, Gelecek Etki VC and Deeptech Labs.

Mads Jensen, Managing Partner of SuperSeed, said:

Critical infrastructure is a multi-billion dollar industry.

In energy, utilities and telecommunications, on-device federated learning has the potential to improve performance, reduce failures, improve security and lead to more efficient and sustainable services. “The OctaiPipe team has already demonstrated significant customer engagement and we are delighted to support them as they grow to address this important market.”

Dr Will Cavendish, Global Digital Services Leader at ARUP, said:

“Water treatment is a complex environment that is costly for water companies to operate and carries significant regulatory risks, including heavy fines for incorrect treatment.

Federated learning, including solutions like OctaPipe, is an AI technology that can help. It enables continuous learning from multiple and dispersed local data sources, better predicting future challenges. As data from the built environment increases, centralized solutions begin to become unmanageable and uneconomical.

Therefore, federated learning reduces costs and dependency on the cloud while maintaining model accuracy, security, and privacy. “Federated learning also improves system resilience, meaning there is no risk of downtime and systems can remain fully operational in the event of an outage or failure.”

Miles Kirby, CEO of ARM-backed Deeptech Labs, said:

“At Deeptech Labs, we are looking for founders to address global challenges with innovative technology. Eric and the OctaiPipe team are global pioneers in federated learning and edge computing.

By applying this technology to critical infrastructure as an easy-to-use platform-as-a-service, OctaiPipe helps ensure that the services and utilities the world depends on can benefit from the latest advances in AI without incurring the costs and risks of running models. on the cloud.”

The money will allow OctaiPipe to further develop its proprietary federated learning technology and scale the availability of the OctaiPipe platform to critical Internet of Things (IoT)-dependent industries, including energy, utilities, telecommunications, manufacturing and original equipment manufacturers ( OEM) of connected devices.

In addition to the financing, Octaipipe has also announced the appointment of Arnaud Lagarde as chief revenue officer. Lagarde will lead Octaipipe’s business development. Prior to joining Octaipipe, Lagarde was Vice President of Sales at Humanising Autonomy, where he led global sales efforts and go-to-market initiatives across automotive, autonomous vehicles and smart city solutions providers.

Main image: OctaiPipe. Photo: uncredited.

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