MLOps on AWS: Deploy and Manage Machine Learning Models

Transitioning a machine learning model from a local notebook to a reliable production environment is one of the most critical challenges in AI today. This text-based course guides you through the core principles of Machine Learning Operations (MLOps) using AWS, helping you transition from understanding basic cloud concepts to building automated pipelines that test, deploy, and monitor models in production. What you'll learn: 1. Understand foundational MLOps terminology, life cycles, and cloud architecture basics. 2. Configure AWS SageMaker to build, train, and deploy machine learning models. 3. Implement CI/CD pipelines to automate model deployment and version control. 4. Package models using container basics with Docker and AWS container services. 5. Monitor model performance and set up alerts for data drift and system health. 6. Apply infrastructure-as-code principles to manage cloud resources systematically. You will start with essential definitions and step-by-step conceptual guides, gradually moving into practical architectural patterns and deployment workflows. Through detailed written explanations and structured text-based scenarios, you will build a solid blueprint for production-ready AI systems. This course is designed for aspiring AI engineers, data scientists, and software developers who are new to MLOps. No prior AWS or DevOps experience is required. Start reading today to bridge the gap between data science and production engineering.