About this event
What if you could predict track failures before they happen without building a single custom model?
Rail infrastructure operates at the intersection of safety, reliability, and operational efficiency. Measuring track quality accurately is essential to ensure safety, avoid delays, and reduce maintenance costs. Traditional predictive models require days of engineering effort, separate builds for each use case, and still fall short when conditions change.
TabPFN is here to change that. That's why Hitachi Rail turned to TabPFN to power predictive maintenance, a single foundation model that can scale across every track without retraining for each one.
In this session, we'll go behind the scenes of a real enterprise scale deployment: from proof of concept to production, the data challenges, and what it takes to bring a foundation model into a mission-critical environment.
Join Isabel Ferrando (Innovation Manager, Hitachi Rail) and Sauraj Gambhir (Co-Founder & COO, Prior Labs) to discover how foundation models are transforming predictive maintenance and what this means for your own predictive workflows.
Prior Labs
Prior Labs is a company that focuses on developing cutting edge tabular AI to make data accessible to everyone.
