Kubeflow: ML Workflow Management
Kubeflow packages a curated set of Kubernetes-native services for machine-learning operations:
- Notebooks: Jupyter and VS Code workspaces backed by persistent storage.
- Pipelines: Directed acyclic graph workflows for training and deployment with Tekton or Argo under the hood.
- Katib: Hyperparameter tuning and experiment tracking.
- Model Serving: KFServing (KServe) for autoscaled inference endpoints.
- Central Dashboard & Profiles: Multi-user workspaces with role-based access.
When to Choose Kubeflow
- You already operate Kubernetes and need end-to-end ML tooling without building everything yourself.
- Teams want self-service notebooks and reproducible pipeline definitions using YAML/Python SDKs.
- Regulatory or security policies require infrastructure to remain inside your VPC instead of a fully managed SaaS.
Considerations
- Kubeflow clusters demand solid platform engineering: manage Istio gateways, certificates, storage classes, and GPU scheduling.
- Upgrades can be complex; pin releases and follow the upstream upgrade guides carefully.
- Harden access (OIDC, network policies) to prevent the misconfigurations highlighted in the Kubeflow crypto-mining advisory.
Getting Started
- Install via Kubeflow manifests or managed distributions (AWS, GCP, Azure variants).
- Use the Kubeflow Pipelines SDK to define reusable workflows and track lineage.
- Integrate with central observability (Prometheus/Grafana, Cloud Logging) for runtime visibility.