Cloud Machine Learning Engineering and MLOps Fundamentals

Scaling machine learning models from a local notebook to a robust, automated cloud production environment is one of the most critical skills in modern technology. This course guides you through the foundational principles of cloud machine learning engineering and operational pipelines. You will transition from writing simple model scripts to designing automated, reproducible ML workflows in the cloud. You will learn how to prepare data, train models using automated machine learning (AutoML), and deploy those models as scalable cloud services. What you'll learn: - Understand the fundamental architecture of cloud-based machine learning systems and MLOps lifecycles - Apply software engineering best practices to write clean, reproducible machine learning code - Configure automated machine learning pipelines to streamline model selection and hyperparameter tuning - Deploy trained machine learning models as scalable, secure cloud APIs and microservices - Implement basic CI/CD pipelines and monitoring strategies specifically tailored for machine learning workflows - Explore modern operational patterns including retrieval-augmented generation (RAG) and LLM deployment basics The course begins with core terminology and architectural concepts before walking you through the practical steps of building, testing, deploying, and monitoring cloud-based models. Through clear written explanations and step-by-step code scenarios, you will gain a practical understanding of production-ready ML workflows. This course is designed for aspiring ML engineers, data scientists, and software developers who want to transition into cloud operations. No prior cloud engineering experience is required, though a basic understanding of Python is helpful. Start building scalable, automated machine learning pipelines in the cloud today.