Overview

Getting Started with Amazon Bedrock is a foundational course designed for developers, data professionals, and AI enthusiasts seeking to build expertise in generative AI using AWS’s fully managed service—Amazon Bedrock. This course focuses on the key components of the Bedrock platform, including foundation model selection, responsible AI principles, Retrieval-Augmented Generation (RAG), agent orchestration, automation, and integration with AWS services. Through a structured and interactive learning path, you’ll gain hands-on experience developing AI-powered applications, managing secure workflows, and understanding the tools necessary to scale generative AI in production environments. Whether you're just beginning your journey in AI or preparing for AWS certification in machine learning, this course provides the core skills and practical insight needed to succeed in the evolving landscape of generative AI. This course includes approximately 5:30–6:00 hours of video lectures, combining both foundational theory and real-world demonstrations. It is divided into three comprehensive modules, each broken down into focused lessons that progressively build your knowledge of Bedrock's capabilities. To assess learning progress, each module includes interactive quizzes and in-video practice questions that reinforce key concepts and application skills. 📘 Module 1: Amazon Bedrock Fundamentals 🧠 Module 2: RAG, Safety & Agents ⚙️ Module 3: Automation, Monitoring & Careers

Syllabus

Amazon Bedrock Fundamentals

This week, we’ll explore the foundational elements of Amazon Bedrock, AWS’s fully managed service for building and scaling generative AI applications with foundation models. You’ll gain a clear understanding of how Bedrock fits into the broader AWS ecosystem and supports serverless, customizable AI solutions. We’ll cover essential topics including the core architecture and pricing model of Bedrock, how to navigate the Bedrock interface, and the use of PartyRock—a no-code playground for quickly prototyping generative AI apps. You’ll also explore responsible AI principles, learn how to evaluate and choose the right foundation models, and see Amazon Bedrock in action through guided demos. By the end of the week, you’ll have a solid understanding of Amazon Bedrock’s capabilities and how to get started with building and experimenting with foundation models in a secure and scalable way.

RAG, Safety & Agents

In Week 2, we’ll shift our focus to Retrieval-Augmented Generation (RAG), safety mechanisms, and agent-based orchestration in Amazon Bedrock. You’ll begin by understanding the architecture and principles behind RAG and how it enhances large language model (LLM) outputs with external knowledge sources. This week also introduces you to Amazon Bedrock Guardrails—a powerful toolset for implementing content safety, privacy filters, and responsible AI controls. You’ll explore how to create and apply Guardrails through practical demos. Finally, you’ll get hands-on with Bedrock Agents, learning how to configure them to automate workflows, enhance interactivity, and support dynamic, multi-step tasks. By the end of this module, you’ll be equipped to build secure, reliable, and intelligent generative AI applications using Amazon Bedrock’s advanced capabilities.

Automation, Monitoring & Careers

Welcome to Week 3! This final module focuses on advanced topics including workflow automation, system integration, and career development within the Amazon Bedrock ecosystem. You’ll learn how to streamline generative AI processes using Amazon Bedrock Flows and implement data-driven automation through Bedrock Data Automation (BDA). We’ll also explore how to monitor and integrate Bedrock applications with AWS services like Amazon CloudWatch and Amazon S3 to ensure operational visibility and performance. Finally, you’ll gain insight into the certifications, career paths, and job opportunities available for professionals working with generative AI and AWS technologies. By the end of the week, you’ll be equipped with the skills to automate, monitor, and scale generative AI solutions—while confidently navigating your career journey in the AWS ecosystem.