From Raw Data to Model Serving: A Blueprint for the AI/ML Lifecycle with Kubeflow

Project Overview A Note on the Data Why Kubeflow? Key Benefits Getting Started: Prerequisites and Cluster Setup Prerequisites 1. Create a Local Kubernetes Cluster 2. Deploy Kubeflow Pipelines 3. Upload the Raw Data to MinIO 4. Install Model Registry, KServe, Spark Operator, and Set Policies Building and Understanding the Pipeline Images Image Locations How to Build Entry points Pushing Images The Kubeflow Pipeline 1. Data Preparation with Spark 2. Feature Engineering with Feast 3. Model Training 4. Model Registration 5. Real-Time Inference with KServe Importing and Running the Pipeline Import the Pipeline Run the Pipeline Testing the Live Endpoint Conclusion 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. The project implements a complete MLOps workflow for a fraud detection use case.