How Network-Trained Time Series Models Are Driving NetOps Transformation – IBM Triple-T
Generic LLMs offer limited value in the network space – they can’t handle time series data and don’t have the contextual awareness needed to produce relevant insights. In this session, we’ll offer a first look into IBM’s new strategy to deliver transformative impacts to network teams using purpose-built time series models which are trained on network data and enriched with semantic context. We’ll show how this reach stream of data analytics combined with a robust reasoning chain produces powerful insights to drive the next generation of network operations through IBM Network Intelligence.
Saurabh Aditya is Principal Product Manager at IBM, specialising in network AI, automation, and hybrid cloud networking. A passionate advocate for “AI for Networking,” he is deeply engaged in exploring how emerging technologies are redefining the future of connectivity. With a career spanning software engineering leadership and product development, he has played pivotal roles in the development of networking protocols, platforms, and operating systems. His experience also includes impactful contributions within the startup ecosystem, particularly in analytics and Software Defined Networking (SDN) space. He is committed to deep technical proficiency, holding CCIE Service Provider #44459.
