This repository aims to develop a step-by-step tutorial on how to build a Kubeflow Pipeline from scratch in your local machine.

If you want to know in detail about the detailed explanation of how to develop your first kubeflow pipeline, I recommend you take a look at the article: Kubeflow Pipelines: How to Build your First Kubeflow Pipeline from Scratch

• Files
• How to use
• Contributing
• Contact
• License

• decision_tree: Contains the files to build the decision_tree component as well as the Dockerfile used to generate the component image.
• logistic_regression: Contains the files to build the logistic_regression component as well as the Dockerfile used to generate the component image.
• download_data: Contains the files to build the download_data component as well as the Dockerfile used to generate the component image.
• pipeline.py: Contains the definition of the pipeline, which when executed generates the FirstPipeline.yaml file.

• It is recommended to have previously installed kfp as well as configured kubeflow on top of kubernets or a minimal version such as minikube.
• All the images are accesible in Docker Hub: https://hub.docker.com/repository/docker/fernandolpz/only-tests

Feel free to fork the model and add your own suggestiongs.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/YourGreatFeature)
3. Commit your Changes (git commit -m 'Add some YourGreatFeature')
4. Push to the Branch (git push origin feature/YourGreatFeature)
5. Open a Pull Request

If you have any question, feel free to reach me out at:

• Twitter
• Medium
• Linkedin
• Email: [email protected]

Distributed under the MIT License. See LICENSE.md for more information.