AWS Machine Learning Engineering: SageMaker and MLOps Workflows

Transitioning from training a machine learning model on your local machine to deploying it in a reliable, scalable cloud environment can be a daunting leap. This course bridges that gap by introducing you to cloud-based machine learning engineering on AWS. You will learn how to take trained models, deploy them to production endpoints, and automate the entire lifecycle. By understanding key cloud concepts and MLOps best practices, you will transition from a data scientist or developer into a cloud machine learning practitioner capable of managing production-grade systems. What you'll learn: - Understand foundational cloud machine learning concepts and core AWS services. - Deploy machine learning models to scalable endpoints using AWS SageMaker. - Automate machine learning workflows and pipelines using AWS Step Functions and Lambda. - Implement modern MLOps practices, including basic containerization and model monitoring. - Configure secure, cost-effective serverless inference architectures for real-time predictions. Starting with essential terminology, you will progress through structured text lessons and code examples that guide you from basic model deployment to orchestrating fully automated pipelines. This course is designed for beginners to cloud engineering, data scientists looking to operationalize their models, and software developers transitioning into machine learning roles, requiring no prior AWS experience. Start reading today to master the essentials of modern cloud machine learning engineering.