Architecting Multi-Cloud AI Pipelines: A Framework for Resilience and Performance at Scale

Abstract

Due to the growing need to deploy scalable and high-performance AI systems, organizations are more frequently considering multi-cloud architectures as the means of achieving agility, resilience, and cost effectiveness. Nevertheless, training robust AI pipelines on heterogeneous cloud environments is fraught with difficulties concerning data synchronization, workload orchestration, fault resiliency and latency reductions. This paper proposes an architectural cohesion of building AI pipelines that are fault-tolerant, high-performance, and dynamic across multi-cloud native environments.