In this session, we explore the GitOps challenges involved in building a unified platform for multi-cloud Artificial Intelligence (AI) and Machine Learning (ML) workflows on Kubernetes. Discover the intricacies of integrating key open source projects—Open Data Hub, Codeflare SDK, KubeStellar, KubeFlow Pipelines (KFP), and Multi-Cluster Application Dispatcher (MCAD). Learn about our source control challenges, such as handling secrets, automation hurdles with service accounts, and post-deployment configuration to achieve a 12-minute deployment cycle. Gain insights into the challenges of orchestrating deployment tasks across clusters and clouds, aiming to engineer a consolidated and scalable multi-cloud Kubernetes ecosystem for AI/ML operations.