Foundation Models and RAG with SageMaker JumpStart

As organizations rush to adopt generative AI, the ability to deploy and customize large-scale models securely is becoming an essential skill. SageMaker JumpStart provides a powerful, simplified pathway to access, fine-tune, and host state-of-the-art foundation models within a secure cloud environment. This text-based course guides you through the entire lifecycle of working with foundation models on AWS. You will transition from understanding basic generative AI concepts to executing fine-tuning jobs and building retrieval-augmented generation (RAG) workflows that ground models in your own domain-specific data. What you'll learn: - Understand the core terminology of foundation models, fine-tuning methodologies, and RAG architectures. - Deploy pre-trained foundation models directly within SageMaker JumpStart using optimized configurations. - Fine-tune models on domain-specific datasets to adapt their behavior and knowledge. - Implement retrieval-augmented generation patterns using vector databases to ground model responses. - Evaluate model performance and manage inference endpoints for production-ready applications. - Apply modern cloud security and MLOps best practices to monitor your deployed AI models. The journey begins with foundational definitions of generative AI and cloud infrastructure. From there, you will read through detailed explanations on configuring SageMaker training jobs, executing parameter-efficient fine-tuning, and integrating external data sources for real-time retrieval-augmented generation. This course is designed for developers, data practitioners, and cloud enthusiasts who are new to generative AI workflows. No prior experience with machine learning frameworks is required, though basic familiarity with cloud concepts is helpful. Start reading today to build secure, domain-specific generative AI solutions.