Machine Learning on AWS with SageMaker: Build and Deploy Models

Modern organizations rely on cloud-based machine learning to scale their intelligence and automate complex decision-making. This course provides a structured path to understanding how to manage the full machine learning lifecycle using SageMaker, the premier platform for building and scaling AI solutions. You will progress from foundational concepts to deploying production-ready models, gaining the practical skills needed to handle data processing, model training, and integration with modern AI frameworks. By the end of this course, you will be able to navigate the complexities of cloud-native machine learning with confidence. What you'll learn: - Understand SageMaker core components and the machine learning lifecycle - Prepare and engineer datasets for training using cloud-native tools - Train and tune models using built-in algorithms and hyperparameter optimization - Deploy machine learning models and large language models like DeepSeek - Implement model monitoring and A/B testing for production environments - Apply modern LLM techniques using Hugging Face integration - Secure machine learning workloads and ensure model explainability The course begins with essential terminology and environment setup before moving into data engineering and model development. You will read through detailed explanations of deploying models and managing modern AI workflows, including how to maintain and update production systems without downtime. This course is designed for beginners, developers, and data enthusiasts who want to start their journey into cloud-based machine learning. No prior experience with SageMaker or advanced data science is required to begin. Start building your cloud machine learning expertise today.