KubeRay
Why Kubernetes is Great for Running AI/MLOps Workloads
Kubernetes has become the de facto platform for deploying AI and MLOps workloads, offering unmatched scalability, flexibility, and reliability. Learn how Kubernetes automates container operations, manages resources efficiently, ensures security, and supports ...
Joydip Kanjilal | | AI containerization, AI model deployment, AI on Kubernetes, AI scalability, AI Workloads, cloud-native ML, container orchestration, data science infrastructure, DevOps for AI, edge AI, fault tolerance, federated learning, GPU management, hybrid cloud AI, Kubeflow, KubeRay, kubernetes, Kubernetes automation, Kubernetes security, machine learning on Kubernetes, ML workloads, MLflow, MLOps, persistent volumes, resource management, scalable AI infrastructure, TensorFlow
Fitting Square Kubernetes Into the Round AI-Native Apps
Kubernetes tamed cloud-native workloads, but AI-native apps push its limits. Can it evolve for GPU-first, data-intensive AI — or is it time for new control planes? ...
Alan Shimel | | AI control plane, AI infrastructure, AI pipelines Kubernetes, AI-native applications, cloud-native vs AI-native, container orchestration AI, distributed training orchestration, GPU scheduling, inference at scale, internal developer platforms, Kubeflow, KubeRay, kubernetes, Kubernetes AI workloads, Kubernetes future, Kubernetes limitations, Kubernetes vs AI, platform engineering, Ray on Kubernetes, Volcano scheduler

