Foundational AI Models with AWS Bedrock

Generative AI is transforming how we build software, but choosing and managing the right underlying models can be overwhelming. This text-based course guides you through navigating the ecosystem of foundational models using Bedrock. You will transition from a curious beginner to a confident practitioner who understands how to evaluate, customize, and deploy large language models. Through clear explanations and written step-by-step walkthroughs, you will learn to align model capabilities with your specific project requirements while keeping costs and performance optimized. What you'll learn: 1. Understand the core concepts of foundational models and how Bedrock simplifies their deployment. 2. Compare and select the right model for your use case based on performance, latency, and cost. 3. Apply prompt engineering techniques and adjust inference parameters to control model outputs. 4. Explore customization methods like fine-tuning and Retrieval-Augmented Generation (RAG) to leverage proprietary data. 5. Manage and optimize operational costs when running generative AI workloads. 6. Implement security and privacy best practices to protect sensitive data within AI applications. The course begins with foundational definitions of generative AI and model architectures, then transitions into practical strategies for selecting, configuring, and securing models within the Bedrock ecosystem. This course is designed for software developers, product managers, and technology enthusiasts who are new to generative AI and want to build a solid foundation without any prior machine learning experience. Start reading today to unlock the potential of foundational models for your next project.