MLOps and Feature Management with Vertex AI

Transitioning machine learning models from a local notebook to a reliable production environment requires robust operations. This text-based course guides you through the core principles of Machine Learning Operations (MLOps) with a special focus on managing features systematically. You will understand how to design, deploy, and monitor scalable ML pipelines. By learning how to centralize and share features, you will prevent training-serving skew and keep your production models highly accurate and reliable. What you'll learn: Understand foundational MLOps principles and the lifecycle of production machine learning systems; Configure and manage centralized feature stores to share and serve features consistently; Deploy scalable machine learning pipelines using Vertex AI orchestration tools; Monitor model performance and detect data drift in real-time production environments; Apply best practices for versioning data, features, and model artifacts. The course starts with essential MLOps definitions and architectural concepts before moving into step-by-step written walkthroughs for feature engineering, pipeline deployment, and continuous monitoring. Designed for aspiring ML engineers, data scientists, and developers new to MLOps, this course requires no prior production deployment experience. Start reading today to master the operational side of machine learning.