Tag: Custom Code

Containerizing Your Code: Docker and Kubeflow Pipelines Kubeflow Pipelines allows you to build, deploy, and manage end-to-end machine learning workflows. In order to use custom code in your pipeline, you need to containerize it using Docker. This ensures that your code can be easily deployed, scaled, and managed by Kubernetes, which is the underlying infrastructure for Kubeflow. In this tutorial, we will guide you through containerizing your Python code using Docker and integrating it into a Kubeflow Pipeline. Prerequisites Docker…