Tutorials

ML Pipeline Tutorials

This section provides comprehensive, hands-on tutorials for building and deploying machine learning pipelines using the KFP Operator. Each tutorial includes complete code examples, step-by-step instructions, and best practices.

Recommended Learning Path

1. Training Pipeline - Build a complete workflow
2. Pipeline Dependencies - Learn chaining

Tutorial Prerequisites

Required for All Tutorials

• Kubernetes cluster with KFP Operator installed
• kubectl configured to access your cluster
• Basic ML knowledge and Python programming skills
• Container registry access for storing pipeline images

Tutorial-Specific Requirements

• Docker: Required for tutorials involving custom pipeline building
• Git: Recommended for version controlling your pipeline code
• Cloud Storage: Some tutorials require cloud storage (GCS, S3, etc.)
• ML Frameworks: TFX knowledge helpful for advanced tutorials

Code Repository

All tutorial code is available in our GitHub repository:

Tutorial Examples

# Clone the repository to follow along
git clone https://github.com/sky-uk/kfp-operator.git
cd kfp-operator/docs-gen/includes/master

Getting Help

If you encounter issues while following the tutorials:

1. Check the troubleshooting section in each tutorial
2. Review Common Issues for known problems
3. Search GitHub Issues for similar problems
4. Ask in GitHub Discussions for community help
5. Check Platform Engineer docs for installation issues

Related Resources

External Tutorials

• TFX Tutorials - TensorFlow Extended framework
• Kubeflow Pipelines Tutorials - ML workflow platform
• Kubernetes Tutorials - Container orchestration

Ready to start building? Choose a tutorial that matches your experience level and dive in!