This hands-on workshop guides participants through building a complete ML pipeline using production-grade tools on Kubernetes. Starting with a Kind cluster, you’ll implement each component of a modern MLOps stack: Airflow for orchestration, Spark for data processing, Ray for model training and serving, and MLflow for experiment tracking. Participants will create a pipeline that processes multiple dataset sizes, trains variant model architectures, and automatically deploys the best performer. Perfect for developers and data scientists looking to bridge the gap between ML experimentation and production deployment.