Rapid development for nuclear-powered data centers with Grafana, machine learning, and Jupyter notebooks

How do we power AI? Next-generation nuclear fission and fusion reactors present a potential solution to sustainably meet escalating energy demands with zero carbon emissions. This is driving a renaissance in nuclear technology, as evidenced by recent collaborations between cloud providers, AI companies, and nuclear energy firms. Traditionally, nuclear materials research has been hampered by slow, manual processes that quickly become obsolete. Today's challenges require adaptable systems and platforms that can scale and evolve with each new discovery and technological breakthrough. 

In this session, Theia Scientific Co-founders Christopher Field and Kevin Field share their company's approach to materials research and non-destructive testing workflows, built on a novel combination of Grafana, machine learning, and Jupyter notebooks. This application development platform enables (data) scientists, engineers, and developers to rapidly create their own custom machine learning-powered applications. 

Grafana delivers the customizable dashboard-driven web-based "single pane of glass" window into the ML-analyzed data collected with electron microscopes and industrial X-ray inspection systems. Grafana’s plugin architecture, “big tent” philosophy for interoperability, and open source observability ecosystem provide the foundation for the platform’s adaptability.

See a live demonstration of the platform that is paving the way for nuclear energy to literally power Grafana-based observability, cloud infrastructure, and AI systems.