Building End-to-End ML Pipelines with Kubeflow: Lessons Learned
When I started working with Kubeflow, I quickly realized there was a gap between theory and what actually happens in practice. This post is about building an end-to-end ml pipeline with kubeflow. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it. Introduction to Kubeflow Pipelines As I delved into the world of Machine Learning Operations (MLOps), I discovered the power of Kubeflow Pipelines in building end-to-end ML workflows. My journey was not without its challenges, but the lessons I learned along the way have been invaluable. In this article, I'll share my experiences, mistakes, and key takeaways from building ML pipelines with Kubeflow. What is Kubeflow Pipelines? Kubeflow Pipelines is a platform that allows you to define, deploy, and manage complex ML workflows. It turns each ML step into a containerized component, making it easy to manage and reuse pipeline comp...