Emerging Cognitive Computing: Machine learning at the edge for the oil and gas industry

SPEAKERS

Ivan Scattergood Chief Technology Officer, OctaiPipe

ABOUT THE SESSION

Ivan Scattergood, CTO of OctaiPipe, brings over 20 years of experience in analytics and monitoring systems, now focused on transforming industrial operations through edge AI. With a background in cybernetics and control engineering, Ivan leads the development of OctaiPipe’s Federated Edge AI platform for IoT. Passionate about innovation, he believes the future lies in bringing machine learning closer to the data source. In this session, he will delve into how cognitive computing and edge-based AI can drive smarter, faster, and more secure decision-making in the oil and gas industry. From enabling real-time insights to reducing cloud dependency, Ivan will explore practical applications and future trends that are shaping the next generation of industrial intelligence.

He will share his insights on how cognitive computing is evolving through decentralized machine learning. Traditionally, organizations have relied on collecting vast amounts of data from distributed devices and centralizing it in the cloud for processing. While effective in the past, this model introduces high data transfer costs, significant privacy concerns, increased latency, and dependency on stable network connections. OctaiPipe, a purpose-built ML Ops platform, offers a cutting-edge alternative by enabling federated learning directly at the edge—on IoT devices themselves. This means that data never needs to leave the device, and models can be trained locally and collaboratively across multiple endpoints. The result is a massive reduction in cloud dependency, improved data security, and better alignment with regulations such as GDPR and the Cyber Resilience Act. It also ensures higher system resilience, faster decision-making, and scalable deployment across remote industrial environments. Ivan will explore how this approach can be game-changing for industries like oil and gas, where distributed assets and real-time insights are critical. By empowering machines to learn locally, OctaiPipe sets a new standard for edge intelligence and compliance-first AI deployment.

Key Topics-

1. Traditionally, data is moved from extremely abundant devices & centralised into the cloud for AI. This is expensive, causes privacy risks, increases latency, depends too much on constant device connection.
 

2. OctaiPipe, is a ML Ops platform designed to implement Federated learning on IoT devices. Its innovative approach to training machine learning models at the edge reduces the need to centralise data.
 

3. This massively reduces the cost of data transfer, increases Cyber security, compliance and it lowers legal risks including the GDPR & Cyber Resilience Act.

For more on this conference or to access the session, reach out to us at [email protected]. 

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