Deploying Kubeflow as an AWS SageMaker Alternative

Kubeflow is the open-source MLOps platform for Kubernetes, a self-hosted alternative to AWS SageMaker that bundles JupyterLab notebooks, KFP pipelines, the Trainer v2 API for distributed training, KServe for model serving, and Katib for hyperparameter optimisation. This guide deploys Kubeflow on a multi-node Kubernetes cluster, creates a user profile, runs a sample pipeline, executes a TrainJob, deploys an InferenceService, and launches a Katib experiment. By the end, you'll have a working Kubeflow platform covering the full ML lifecycle on your own cluster.

Prerequisite: Kubernetes cluster (v1.31+) with 3+ nodes and at least 4 CPU / 16 GB RAM per node. kubectl and kustomize (v5.4.3+) on your workstation. A default StorageClass for PVC provisioning.

SageMaker → Kubeflow Mapping

SageMaker

Kubeflow