MLOps on Cloud Platform: Building End-to-End Machine Learning Pipelines

Taking a machine learning model from a local notebook to a reliable production environment requires more than just training code. This course guides you through the essential principles of Machine Learning Operations (MLOps) using Cloud Platform. You will transition from manual model training to building fully automated, scalable, and monitored ML pipelines, gaining the confidence to manage the entire lifecycle of machine learning models in a cloud environment. What you'll learn: - Understand foundational MLOps concepts, lifecycle stages, and key terminology. - Configure automated data ingestion and preprocessing pipelines on Cloud Platform. - Build and orchestrate ML workflows using Vertex AI Pipelines. - Deploy models to production endpoints for real-time and batch predictions. - Monitor model performance, track data drift, and implement automated alerting. - Apply continuous integration and continuous deployment (CI/CD) practices to ML code. The course begins with foundational definitions and MLOps architecture before guiding you step-by-step through pipeline construction, model deployment, and production monitoring. You will learn through clear written explanations, structured conceptual breakdowns, and practical configuration examples. This course is designed for beginning data scientists, software engineers, and cloud enthusiasts looking to enter the field of MLOps, with no prior DevOps or cloud engineering experience required. Start your journey toward mastering production-grade machine learning workflows today.