Multi-Cloud Machine Learning Data and Workflow with Kubernetes
summary
Covers building a multi-cloud ML platform on Kubernetes for autonomous-vehicle model training across on-prem and public clouds with different GPUs and network interfaces (Infiniband, RoCE). Describes concrete solutions for managing training data across environments, handling multi-user and gang scheduling, and supporting heterogeneous hardware.
AI-written summary · about
related talks
36mMulti-Cloud Machine Learning Data and Workflow with Kubernetes
46mMachine Learning with Kubernetes
31mBuilding and Managing a Centralized ML Platform with Kubeflow at...
06mKubernetes Love Machine Learning, Even on Private Cloud
46mDeploying scalable Machine Learning models with Kubernetes
53mUsing Kubernetes for Machine Learning Frameworks
26mMachine Learning Meets K8s
31mKubernetes For GPU Powered Machine Learning Workloads In Academia