Are you looking for a practical, reproducible way to take a machine learning project from raw data all the way to a deployed, production-ready model? This post is your blueprint for the AI/ML lifecycle: you’ll learn how to use Kubeflow and open-source tools such as Feast to build a workflow you can run on your laptop and adapt to your own projects.
We’ll walk through the entire ML lifecycle — from data preparation to live inference — leveraging the Kubeflow platform to create a cohesive, production-grade MLOps workflow.