Choosing and Optimizing Foundation Models in Amazon Bedrock

Generative AI is transforming how we build software, but choosing the right model for your specific business needs can be challenging. This course guides you through navigating, selecting, and optimizing foundation models using Amazon Bedrock. You will gain a clear understanding of how to leverage pre-trained models to build secure, scalable, and highly efficient applications. By reading this course, you will understand the core capabilities of Bedrock, compare different model families, and master foundational concepts like prompt engineering and retrieval-augmented generation (RAG) to deliver high-performing AI solutions. What you'll learn: - Understand the fundamental concepts of foundation models and how Bedrock simplifies their deployment - Compare and select the ideal model family for text, image, and multimodal use cases - Apply basic prompt engineering techniques to optimize model responses - Discover the principles of Retrieval-Augmented Generation (RAG) to connect models to custom data sources - Evaluate model performance and cost-efficiency to scale your AI applications responsibly You will start with essential terminology and the foundational pillars of cloud-based generative AI, then progress to practical architectural patterns, model evaluation strategies, and deployment best practices. This course is designed for beginners, developers, and technology decision-makers who want to understand AI integration without needing prior machine learning experience. Start reading today to build a solid foundation in modern generative AI implementation.