AWS Machine Learning and MLOps Foundations

As organizations scale their artificial intelligence initiatives, the ability to build, deploy, and maintain machine learning models in the cloud is more critical than ever. This course introduces you to the core principles of machine learning and MLOps on AWS, bridging the gap between data science and cloud engineering. Through clear written explanations and practical architectural walkthroughs, you will understand how to design and manage end-to-end ML lifecycles. What you'll learn: Understand foundational machine learning concepts, terminology, and the AWS AI/ML service landscape; Configure data ingestion, preprocessing, and storage pipelines using AWS data services; Build and train machine learning models using SageMaker and built-in algorithms; Deploy models to production endpoints and manage inference workloads effectively; Implement basic MLOps practices, including model monitoring, versioning, and pipeline automation; Explore foundational alignment with associate-level AWS machine learning exam domains. The curriculum begins with essential machine learning definitions and cloud basics before moving into data preparation, model training, deployment strategies, and operational best practices. This course is designed for aspiring cloud engineers, data scientists, and developers who are new to AWS ML services and want to establish a strong foundational understanding without any prior cloud experience. Start reading today to unlock the potential of cloud-scale machine learning.