TL;DR:
- OctaiPipe secures £3.5M in pre-Series A funding for their Edge AI platform.
- This includes £3 million in funding and a £500,000 grant from Innovate UK.
- The platform empowers data scientists and AI engineers in Critical Infrastructure.
- OctaiPipe’s Federated Learning Operations (FL-Ops) platform deploys AI at the Edge.
- It manages machine learning across scalable IoT networks, reducing latency and data costs.
- Traditional edge computing limitations and data privacy concerns are overcome.
- Investors, including SuperSeed and Deeptech Labs, recognize the potential for enhancing performance, security, and sustainability.
- OctaiPipe aims to expand across Energy, Utilities, Telecoms, Manufacturing, and more.
- Arnaud Lagarde joins as Chief Revenue Officer to drive commercial development.
Main AI News:
In a recent development, OctaiPipe, the end-to-end Edge AI platform catering to the needs of the industrial IoT sector, has successfully raised £3 million in pre-Series A funding, coupled with an additional £500,000 grant from Innovate UK. Launched in 2022, OctaiPipe is specifically designed to empower data scientists and AI engineers working in Critical Infrastructure, providing them with a reliable and comprehensive Federated Learning Operations (FL-Ops) platform.
OctaiPipe’s groundbreaking platform facilitates the swift deployment and automation of AI solutions at the Edge, while also managing distributed machine learning across scalable networks of intelligent IoT devices. To delve deeper into this game-changing technology, we sat down with Eric Topham, the CEO and co-founder of OctaiPipe.
Understanding Federated Learning and Its Crucial Role in IoT
Before delving into the unique aspects of OctaiPipe’s technology, let’s revisit the concept of edge computing. Edge computing is all about positioning intelligence and processing capabilities closer to the source of data, thereby enhancing the capacity for real-time analytics and actionable insights. This approach proves particularly beneficial in scenarios involving rugged environments, as it reduces data transmission to and from the cloud, minimizing latency, and conserving time, energy, and bandwidth.
However, as Eric Topham highlights, automation systems, especially those in Critical Infrastructure, often generate vast volumes of heterogeneous data with rare signals and behaviors. When attempting to build a model on a single edge instance, the lack of relevant events or signals hampers performance. Traditionally, this data is sent to a central data store, and once the model parameters are obtained, it is streamed back to the cloud for inference or shipped down to the edge. This process necessitates frequent model updates, resulting in increased data transmission, network costs, and storage expenses.
Furthermore, achieving the required scale becomes challenging within a single entity, constrained by issues like data privacy, security, and access rights. In the context of mission-critical infrastructure such as utilities, defense, and telecommunications, these barriers to data sharing hinder progress. Topham emphasizes the need for performance, which, in turn, demands scale. Traditional edge computing struggles to provide this scale, and shifting data to the cloud faces risks and costs at scale, leaving untapped potential.
OctaiPipe: Pioneering Federated Learning for Enhanced Edge AI
OctaiPipe introduces a novel, decentralized approach to training AI models called Federated Learning, which eliminates the need for data exchange between IoT devices and cloud servers. In this paradigm, data on IoT devices is used to train AI models locally at the Edge, optimizing performance, enhancing system resilience, bolstering data security, and dramatically reducing cloud data costs.
Topham underscores the critical importance of securing the data that underpins Critical Infrastructure. OctaiPipe empowers data scientists in sectors such as Energy, Utilities, Telecoms, and Security with a secure end-to-end platform for designing, deploying, and managing Federated Learning locally across Edge device networks and at scale. Available as a Microsoft Azure, AWS, or Private Cloud Platform-as-a-Service (PaaS), the OctaiPipe platform is already deployed with over 20 customers and device OEMs.
Investors Rally Behind OctaiPipe’s Vision
The pre-Series A funding round was spearheaded by SuperSeed, with Forward Partners, D2, Atlas Venture, Martlet Capital, Gelecek Etki VC, and Arm-backed Deeptech Labs also joining in. Mads Jensen, Managing Partner at SuperSeed, recognizes the potential of Critical Infrastructure and the role of on-device Federated Learning in enhancing performance, reducing failures, bolstering security, and promoting efficiency and sustainability. He commends OctaiPipe’s strong customer traction and pledges support for their journey to address this vital market.
Dr. Will Cavendish, Global Digital Services Leader at ARUP, highlights the potential of Federated Learning, especially in complex environments like water treatment. It offers continuous learning from dispersed local data sources, improving predictive capabilities while reducing costs and cloud dependence.
Miles Kirby, CEO of ARM-backed Deeptech Labs, acknowledges OctaiPipe’s leadership in Federated Learning and Edge computing, underlining the company’s commitment to addressing global challenges with groundbreaking technology.
The Road Ahead for OctaiPipe
The funding secured by OctaiPipe will be instrumental in further developing their proprietary Federated Learning technology and expanding the availability of the OctaiPipe platform for critical IoT-dependent industries, including Energy, Utilities, Telecoms, Manufacturing, and connected device OEMs. In addition to this milestone, OctaiPipe welcomes Arnaud Lagarde as Chief Revenue Officer, responsible for driving commercial development.
As OctaiPipe continues to innovate and pave the way for secure and efficient Federated Learning in the realm of Critical Infrastructure, the possibilities for improved performance and resilience in vital sectors like Energy, Utilities, and Telecoms are boundless. Stay tuned for more updates on OctaiPipe’s journey as they redefine the future of AI at the Edge.
Conclusion:
OctaiPipe’s successful funding round and innovative Federated Learning technology have the potential to transform the market for Edge AI platforms. With a focus on Critical Infrastructure and overcoming traditional limitations, OctaiPipe is poised to drive improvements in performance, security, and efficiency across various industries, promising a bright future for AI at the Edge.
