The shift toward AI workloads has fundamentally changed how we approach cloud infrastructure design. Traditional architectures, optimized for web applications and microservices, often fall short when handling the unique demands of AI training and inference. Drawing from my experience architecting large-scale AI infrastructure, this article explores the technical considerations and
In today's rapidly evolving AI landscape, organizations are increasingly faced with the challenge of building robust, scalable infrastructure to support their machine learning initiatives. While cloud-managed ML services offer convenience, many enterprises require more control, flexibility, and cost optimization than these services can provide. This is where Kubernetes
