Generative AI on AWS: Building Models with SageMaker

Generative artificial intelligence is transforming how we build software, but deploying these massive models efficiently requires robust cloud infrastructure. This course guides you through the process of developing, tuning, and hosting generative models on AWS using SageMaker. You will transition from understanding basic machine learning definitions to deploying production-ready generative models. Through structured written explanations and step-by-step configuration walkthroughs, you will gain the practical skills needed to manage the lifecycle of generative AI projects in the cloud. What you'll learn: - Understand foundational generative AI concepts, including GANs, VAEs, and modern Transformer architectures - Configure SageMaker environments to train and fine-tune large language models efficiently - Apply parameter-efficient fine-tuning techniques like LoRA to adapt foundation models - Deploy generative models to scalable SageMaker endpoints for real-time inference - Implement monitoring and optimization strategies to track model performance and manage cloud costs - Explore modern patterns like Retrieval-Augmented Generation using cloud data sources We begin with core terminology and the architectural basics of generative models before moving into SageMaker tooling, dataset preparation, fine-tuning, and production hosting. You will read through clear explanations and analyze configuration scripts designed to build your cloud AI confidence. This course is designed for software developers, data enthusiasts, and aspiring cloud engineers who want to learn generative AI workflows on AWS. No prior experience with SageMaker or advanced machine learning is required. Start your journey into cloud-based generative AI today.