AWS Machine Learning Engineering: Model Development and SageMaker

Transitioning from local machine learning prototypes to robust, production-grade cloud solutions requires a structured approach to model development. This text-based course guides you through the core concepts of engineering machine learning systems on AWS. You will gain the skills to select the right algorithms, configure training jobs, optimize model performance, and establish reliable deployment pipelines in the cloud. What you'll learn: • Understand foundational AWS machine learning services and core architectural patterns. • Select and configure SageMaker algorithms for diverse data science tasks. • Optimize model performance using hyperparameter tuning and distributed training techniques. • Deploy models to production endpoints using scalable, cost-effective hosting options. • Implement modern MLOps practices, including model monitoring for drift and pipeline automation. • Practice evaluating model metrics to ensure high-quality predictions over time. The course begins with essential terminology, architectural foundations, and security concepts on AWS before moving into model training, tuning, and deployment strategies. You will read detailed explanations, analyze architectural patterns, and work through practical scenarios designed to build your skills. This course is designed for aspiring cloud engineers, developers, and data practitioners who are new to AWS machine learning services, with no prior cloud deployment experience required. Start reading today to build your foundation in AWS machine learning engineering.