Kubeflow Without Kubernetes? Deploy a Complete MLOps Suite in 60 Seconds with Gubernator
The "Kubernetes Tax" on Modern Machine Learning If you’ve ever tried setting up Kubeflow on Kubernetes, you know the drill: 30+ Custom Resource Definitions (CRDs) Istio Service Mesh + Knative + Cert-Manager + Dex 16 GB to 32 GB of RAM consumed before you even write a single line of Python Days spent debugging webhook admission controllers and Kustomize overlays. Kubernetes is great at…
Kubeflow, while powerful for large-scale machine learning operations, often requires excessive resources and complex setup for smaller teams and startups. Gubernator, a lightweight container orchestrator, offers a simplified alternative that can deploy an entire MLOps suite in just 60 seconds using a single docker-compose.yml configuration.
Gubernator combines the simplicity of Docker Swarm with the intelligent task scheduling and GPU targeting of Nomad, and adds built-in CoreDNS service discovery, multi-node Caddy Ingress with automatic HTTPS/TLS, and persistent shared storage mobility. With Gubernator, the architecture includes MinIO S3 object storage, MLflow experiment tracking, JupyterLab workspaces for PyTorch, and Ollama for LLM inference, all accessible via a single-command deployment.
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