[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-kubeflow":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"The ML toolkit that turns a Kubernetes cluster into an AI platform.","Kubeflow brings the ML lifecycle to Kubernetes: Pipelines orchestrates training workflows as containerized DAGs, Notebooks provides isolated Jupyter environments, Katib automates hyperparameter tuning and KServe serves models with autoscaling. Multi-user with per-namespace isolation, it's our choice to industrialize a team's ML on an existing cluster, cloud or on-premise, without vendor lock-in.",[7,8,9,10],"Pipelines: containerized, versioned, replayable ML workflows.","Katib: distributed hyperparameter tuning and NAS on the cluster.","KServe: model serving with autoscaling, canary and scale-to-zero.","Multi-user: isolated notebooks, RBAC and quotas per namespace.","Shared ML platform on Kubernetes, distributed training, serving.",1792675979325]