AWS Model Training, Optimization, and Deployment

Taking a machine learning model from a local notebook to a reliable production environment can be a daunting challenge. This course guides you step-by-step through the process of training, optimizing, and deploying models on the AWS cloud platform. Through clear written explanations and practical code examples, you will learn how to leverage AWS services to build scalable and cost-effective machine learning pipelines. You will transition from understanding core cloud concepts to implementing real-world deployment strategies. What you'll learn: - Understand foundational machine learning concepts and AWS SageMaker architecture; - Train models efficiently using built-in algorithms and custom containers on AWS; - Optimize model performance and reduce inference costs using hyperparameter tuning and model compilation; - Deploy models to production using real-time endpoints, batch transform, and serverless options; - Implement modern MLOps practices including model monitoring and data drift detection. The course starts with key terminology and foundational AWS machine learning services. You will then progress through data preparation, training configurations, optimization techniques, and various production deployment patterns. This course is designed for aspiring machine learning engineers, data scientists, and developers who are new to AWS. No prior cloud engineering experience is required. Start reading today to master the lifecycle of machine learning on AWS.