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# Add the Helm repository helm repo add --force-update spark-operator https://kubeflow.github.io/spark-operator # Install the operator into the spark-operator namespace and wait for deployments to be ready helm install spark-operator spark-operator/spark-operator \ --namespace spark-operator \ --create-namespace \ --wait # Create an example application in the default namespace kubectl apply -f htt

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK. The Kubeflow pipelines service has the following goals: End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pi

Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, P

Kubeflow is the foundation of tools for AI Platforms on Kubernetes. AI platform teams can build on top of Kubeflow by using each project independently or deploying the entire AI reference platform to meet their specific needs. The Kubeflow AI reference platform is composable, modular, portable, and scalable, backed by an ecosystem of Kubernetes-native projects that cover every stage of the AI life