Cloud Machine Learning Engineering and MLOps Fundamentals

Transitioning a machine learning model from a local notebook to a reliable cloud production environment requires a solid understanding of software engineering and operational best practices. This text-based course guides you through the core concepts of Cloud ML Engineering and MLOps, helping you bridge the gap between data science and system operations.\n\nYou will progress from foundational cloud concepts to deploying automated pipelines, ensuring your models remain accurate, scalable, and secure in production. Through written explanations, step-by-step walkthroughs, and practical configuration exercises, you will develop the skills needed to operationalize machine learning workflows.\n\nWhat you'll learn:\n- Understand the core principles of MLOps, cloud architecture, and the machine learning lifecycle\n- Configure automated machine learning (AutoML) solutions to streamline model selection and training\n- Package machine learning applications using Docker container fundamentals for consistent deployment\n- Build continuous integration and continuous delivery (CI/CD) pipelines tailored for machine learning assets\n- Monitor model performance in production and implement basic data drift detection strategies\n- Explore modern AI patterns including vector databases and basic retrieval-augmented generation (RAG) workflows\n\nThe course starts with essential terminology, cloud service models, and foundational MLOps definitions before moving into containerization, pipeline automation, and production monitoring. You will learn entirely through comprehensive text lessons, structured readings, and hands-on configuration exercises.\n\nThis course is designed for aspiring ML engineers, data scientists, and software developers who are new to cloud operations and want to learn MLOps from the ground up, with no advanced cloud prerequisites required.\n\nStart building reliable, automated cloud machine learning systems today.