From PyTorch to Production: Closing the Evidence Gap for ISO/PAS 8800
As automotive AI moves from research environments into safety-critical production systems, ISO/PAS 8800 is bringing greater focus to traceability, reproducibility and evidence across the full development lifecycle.
In this interview, Peter Kristiansen, Business Dev Exec – Automotive & Defence at Embedl discusses the practical gap between validating an AI model in PyTorch and proving the performance of the compiled, quantised binary running on vehicle hardware. He explains why compliance cannot be treated as an audit-stage task, and how automated evidence capture can help engineering teams continue innovating while building a stronger safety case for embedded AI deployment.