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How to Deploy Kubeflow as a Self-Hosted Alternative to AWS SageMaker

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Kubeflow is an open-source MLOps platform built for Kubernetes that replicates core AWS SageMaker capabilities, including notebook environments, pipeline orchestration, distributed training, model serving, and hyperparameter optimization. A step-by-step technical guide outlines how to deploy Kubeflow on a multi-node Kubernetes cluster running version 1.31 or later, with at least three nodes each having 4 CPUs and 16 GB of RAM. The installation uses kustomize to apply manifests from the official Kubeflow repository, with a retry loop included to handle potential CRD race conditions during setup. Once deployed, users can create profiles, launch JupyterLab notebooks, run Kubeflow Pipelines, execute distributed TrainJobs via the Trainer v2 API, and serve models through KServe. The guide covers the full machine learning lifecycle end-to-end, offering teams a cost-controllable, self-hosted alternative to managed cloud ML platforms.

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