Introduction to SageMaker and the Machine Learning Lifecycle

Transitioning machine learning models from local environments to scalable cloud infrastructure can feel overwhelming. This comprehensive text-only course guides you through the entire machine learning lifecycle using SageMaker on AWS, showing you how to build, train, and deploy models with confidence. You will begin with key terminology and core cloud concepts before diving into practical workflows. By reading through structured explanations and clear code examples, you will learn how to manage data, optimize training jobs, and maintain models in production. What you'll learn: - Understand the core phases of the machine learning lifecycle and modern MLOps principles - Prepare and clean datasets using built-in cloud data processing tools - Train and tune machine learning models efficiently using scalable cloud compute resources - Deploy trained models to production-ready endpoints for real-time and batch predictions - Monitor model performance and detect data drift in live environments - Implement basic automation pipelines to streamline your machine learning workflows This course is designed for beginners, developers, and aspiring data scientists who want to learn cloud-based machine learning workflows. No prior cloud engineering experience is required. Start reading today to build and deploy your first cloud-native machine learning model.