Deploying Kubeflow as an AWS SageMaker Alternative

Short summary

Kubeflow is an open-source MLOps platform for Kubernetes providing a self-hosted alternative to AWS SageMaker, bundling JupyterLab, Kubeflow Pipelines, distributed training, model serving (KServe), and hyperparameter optimization (Katib). This guide walks through deploying Kubeflow on a multi-node Kubernetes cluster, creating user profiles, running sample pipelines, executing distributed training jobs, and launching optimization experiments. By following these steps, you'll have a fully functional MLOps platform covering the complete ML lifecycle on your own infrastructure.

• •Open-source alternative to AWS SageMaker with full MLOps stack (pipelines, training, serving, HPO)
• •Step-by-step deployment guide for Kubernetes clusters v1.31+ with 3+ nodes and 16GB RAM minimum
• •Covers complete ML lifecycle: notebooks, distributed training, model serving, and hyperparameter experiments

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