Generative AI Model Adaptation and Resilient Training with SageMaker

Scaling and adapting large foundation models requires a solid understanding of both tuning methodologies and robust infrastructure. This course introduces you to the core concepts of generative AI model adaptation and the resilient infrastructure needed to train models at scale. You will transition from understanding basic pre-trained models to knowing how to fine-tune them and manage distributed training environments. Through clear, written explanations and practical conceptual walkthroughs, you will develop the skills to select the right adaptation strategies and leverage cloud infrastructure for stable, long-running training jobs. What you'll learn: Learn key terminology and foundational concepts of generative AI and foundation models. Apply model adaptation techniques including fine-tuning and Parameter-Efficient Fine-Tuning (PEFT). Configure SageMaker JumpStart to deploy and adapt pre-trained models. Understand distributed training concepts and how HyperPod ensures resilience. Analyze training performance and troubleshoot common infrastructure bottlenecks. The course begins with foundational definitions of model adaptation before guiding you through practical configuration steps for JumpStart and HyperPod. You will read through detailed architectural explanations and conceptual scenarios designed to prepare you for real-world AI deployment challenges. This course is designed for beginners, with no prior experience in cloud training required. Start learning how to adapt and train generative AI models reliably today.