Kubeflow Setup & Configuration

Kubeflow ships with dozens of components — you probably need four or five. We deploy Kubeflow Pipelines for workflow orchestration, Jupyter notebooks for experimentation, Katib for hyperparameter tuning, and KServe for model serving. Training Operator handles distributed training jobs. We skip components you won't use to reduce cluster overhead and attack surface.

Kubeflow's auth layer (Istio + Dex) gets configured against your identity provider — OIDC, LDAP, or GitHub OAuth. Each team gets an isolated namespace with resource quotas. RBAC policies ensure data scientists can launch training jobs but can't modify cluster infrastructure. Profile management controls who sees what.

Pipeline artifacts, model checkpoints, and datasets need persistent storage. We configure MinIO or S3-compatible backends for artifact storage, PersistentVolumeClaims for notebook data, and shared volumes for datasets. Storage classes get tuned for throughput on training data reads versus durability on model artifacts.

You get a running Kubeflow instance with monitoring (Prometheus + Grafana dashboards for pipeline success rates, GPU utilization, and notebook uptime), log aggregation, and backup procedures for pipeline definitions and metadata. Documentation covers day-two operations: scaling nodes, upgrading components, and troubleshooting common failures.