Automating ML Pipelines with Airflow and Kubernetes

Transitioning machine learning models from local scripts to automated, reliable production pipelines can feel overwhelming. Managing data dependencies, scheduling runs, and scaling infrastructure requires a structured workflow automation strategy. This text-based course guides you through the foundational concepts of MLOps and workflow orchestration, helping you build the skills to package your models, schedule complex data pipelines, and deploy scalable workflows that run automatically. What you'll learn: - Understand core MLOps principles and the role of workflow orchestration in machine learning. - Containerize machine learning applications and dependencies using Docker. - Design and schedule robust directed acyclic graphs (DAGs) using Airflow. - Deploy and manage scalable containerized pipelines using Kubernetes. - Explore managed cloud workflow solutions like Cloud Composer for simplified administration. - Apply basic CI/CD concepts to automate testing and deployment of your pipeline code. The course starts with key terminology, basic pipeline concepts, and foundational orchestration definitions. From there, you will progress through step-by-step written explanations and practical code snippets to build, containerize, and schedule your own automated workflows. This course is designed for aspiring machine learning engineers, data scientists, and developers looking to transition from manual model training to automated pipelines, with no prior orchestration experience required. Start reading today to master the essential tools that keep modern machine learning systems running smoothly.