AWS Generative AI: Practical Step-by-Step Lab Guide

Generative artificial intelligence is transforming how we build software, but getting started with cloud-based AI services can feel overwhelming. This course simplifies the process by guiding you through the essential concepts and practical configurations of generative AI on AWS. Through clear, text-based explanations and step-by-step code walkthroughs, you will transition from an absolute beginner to confidently configuring generative AI workflows. You will understand how to leverage powerful cloud services to build intelligent, context-aware applications. What you'll learn: 1. Understand foundational generative AI concepts, including large language models and multimodal systems. 2. Configure AWS Bedrock to access and deploy leading foundation models. 3. Apply prompt engineering techniques directly within your cloud-powered applications. 4. Build retrieval-augmented generation (RAG) patterns to connect models to custom data sources. 5. Implement vector databases to manage and query high-dimensional embeddings. 6. Practice writing clean Python code to integrate AI services into your software. The course begins with essential AI terminology and foundational cloud concepts before moving into structured, step-by-step walkthroughs. You will read detailed explanations of real-world scenarios, study complete code configurations, and learn how to secure and scale your AI implementations. This course is designed for developers, cloud enthusiasts, and tech professionals who are new to generative AI on AWS. No prior machine learning experience is required, though a basic understanding of cloud concepts and Python is helpful. Start reading today and begin building your own cloud-hosted generative AI applications.