From raw data to model serving with OpenShift AI

This blog post provides a comprehensive guide to transitioning a machine learning project from raw data to a production-ready model using Red Hat OpenShift AI. It details a complete MLOps workflow for a fraud detection system, involving data preparation, feature engineering, model training, and real-time inference deployment. Key steps include setting up the OpenShift environment, creating a Data Science Cluster, configuring necessary components, and implementing a data science pipeline for efficient model serving and management. The workflow emphasizes reproducibility, infrastructure abstraction, and scalability, making it suitable for various machine learning projects.