ModelOps for enhanced decision-making and governance in emergency control rooms

Abstract For mission critical (MC) applications such as bushfire emergency management systems (EMS), understanding the current situation a disaster unfolds is to saving lives, infrastructure and environment. Incident control-room operators manage complex information systems, especially with emergence of Big Data. They are increasingly making decisions supported by artificial intelligence (AI) machine learning (ML) tools for data analysis, prediction decision-making. As volume, speed complexity increases due more frequent fire events, greater availability myriad IoT sensors, smart devices, satellite burgeoning use social media, advances in AI ML that help Data support decision-making perceived “Black Box”. This paper aims scope requirements EMS improve governance AI/ML. An analysis ModelOps technology, used commercial sector, undertaken determine what components might be fit-for-purpose. The result novel set features, an EMS-ModelOps framework resolves than 75% issues whilst being sufficiently generic apply other types mission-critical applications.