Documentation (master)

KFP Operator Documentation

The Kubeflow Pipelines Operator provides a Kubernetes-native API for Kubeflow and VertexAI pipelines. Define and manage ML pipelines as code using kubectl.

Key Features

• Infrastructure as Code: Apply Kubernetes patterns to ML workflows
• Event-Driven: Automated pipeline execution and management
• Enterprise Ready: RBAC, security policies, and multi-tenant isolation
• Developer Friendly: Use kubectl, Helm, and existing CI/CD pipelines

Choose Your Path

For ML Engineers & Data Scientists

Build and deploy ML pipelines using the KFP Operator

Choose this path if you:

• Develop and deploy machine learning pipelines
• Use TFX, Kubeflow Pipelines, or similar ML frameworks
• Need to run experiments and manage model training
• Want to automate ML workflows with GitOps

Includes:

• Quick Start Guides
• Practical Tutorials
• Best Practices
• API Reference
• Troubleshooting

For Platform Engineers & Developers

Install, configure, and maintain the KFP Operator platform

Choose this path if you:

• Install and configure the KFP Operator in Kubernetes clusters
• Manage platform infrastructure and operations
• Develop extensions or contribute to the operator
• Set up multi-tenant ML platforms for teams

Includes:

• Installation Guides
• Architecture Deep-Dives
• Configuration Reference
• Security & RBAC
• Maintenance & Operations

API Reference

Complete technical reference for all Custom Resource Definitions

For developers and advanced users who need:

• Complete API specifications and CRDs
• Technical documentation cross-references
• Consolidated access to all specifications

Architecture

The KFP Operator extends Kubernetes with custom resources for ML pipeline entities:

• Custom Resources: Kubernetes-native representations of pipelines, runs, and configurations
• Controller: Manages resource lifecycle and orchestrates workflows
• Provider Service: Abstracts different ML platforms (KFP, Vertex AI)
• Event System: Publishes pipeline events for reactive workflows

Community and Support

• Discussions: GitHub Discussions for questions and ideas
• Issues: GitHub Issues for bug reports and feature requests
• Releases: GitHub Releases for latest versions

Contributing

Open source project welcoming contributions from ML practitioners and platform engineers.

• Source Code: GitHub Repository
• Contributing Guide: See CONTRIBUTING.md in the repository