ML Pipelines on the Cloud with Vertex AI

In the world of modern AI, building a model is only the first step; the real challenge lies in automating and scaling the entire lifecycle. Transitioning from manual notebook experiments to robust, automated workflows is essential for any aspiring machine learning engineer. This text-only course guides you through the foundational concepts of Machine Learning Operations (MLOps) and shows you how to design, build, and run production-ready ML pipelines on the Cloud. You will learn how to automate data preparation, model training, evaluation, and deployment using modern cloud services. What you'll learn: • Understand the core principles of MLOps and pipeline orchestration • Configure data ingestion and preprocessing components using standard cloud tools • Build automated training pipelines with Vertex AI and Kubeflow • Deploy trained models to scalable endpoints for real-time serving • Implement pipeline monitoring and metadata tracking to ensure model reliability • Apply modern CI/CD practices to automate pipeline execution and updates. You will start with the basic terminology of machine learning pipelines and step-by-step explore pipeline architecture, component design, and orchestrator deployment. The written lessons provide conceptual clarity and practical configuration examples to help you construct your first automated workflow. This course is designed for beginner data scientists, software engineers, and cloud enthusiasts who want to learn the basics of MLOps. No prior pipeline engineering experience is required, though a basic understanding of Python is helpful. Start reading today to master the foundations of cloud-based ML automation.