In this article, I go through every step that is necessary to have a functioning pipeline :

• Create a Kuberneter cluster
• Install Kubeflow
• Create a container registry
• Build a container image and push it to your registry
• Give Kubeflow access to your S3 buckets
• Create Kubeflow components with input and output artifacts
• Create a Kubeflow pipeline, upload it and run it

This article has been peer-reviewed and published in “Towards Data Science”.

Link to the article

• MLOPS
• Kubeflow
• Machine learning