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

Jul 15, 2025 • Helber Belmiro • 22 min read mlops pipelines spark feast model-registry kserve 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. Fraud detection is a critical application in financial services, where organizations need to identify potentially fraudulent transactions in real-time while minimizing false positives that could disrupt legitimate customer activity. Our fraud detection system leverages machine learning to analyze large volumes of transaction data, learn patterns from historical behavior, and flag suspicious transactions that deviate from normal patterns. The model considers various features such as transaction amounts, location data, merchant information, and user behavior patterns to make predictions. This makes fraud detection an ideal use case for demonstrating MLOps concepts because it requires: The workflow ingests raw transaction data, proceeds through data preparation and feature engineering, then model training and registration, and finally deploys the model as a production-ready inference service that can evaluate transactions in real-time. The entire workflow is orchestrated as a Kubeflow Pipeline, which provides a powerful framework for defining, deploying, and managing complex machine learning pipelines on Kubernetes. Here is a high-level overview of the pipeline: The pipeline assumes that the initial datasets ( train. csv , test. csv , etc. ) are already available.