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Introduction to Generative AI with Amazon Bedrock

Packt via Coursera

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Overview

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Embark on a hands-on journey into Generative AI using Amazon Bedrock and Python. Learn to build conversational chatbots, leverage RAG architecture, automate workflows, and generate structured outputs and images. Gain practical skills and a solid foundation for AI application development. This course begins by introducing the fundamentals of Amazon Bedrock and setting up the required Python environment. Learners explore how to interact with Bedrock's API and understand LLMs through engaging, easy-to-follow examples. Early lessons provide the building blocks for creating AI applications. Next, students dive into practical projects, starting with conversational chatbots and RAG-based solutions. Modules cover generating structured outputs, AI agents, and multimodal content including images. Data automation using Bedrock Data Automation (BDA) is demonstrated, ensuring learners can streamline AI workflows efficiently. The final part of the journey focuses on observability, safeguards, and troubleshooting. Learners discover how to implement programmatic guardrails and handle common errors, concluding with practical exercises and inspiring use cases. By course end, students are equipped to confidently prototype and deploy AI applications using Amazon Bedrock. This course is ideal for programmers, technical managers, and AI enthusiasts seeking practical knowledge of Generative AI with Amazon Bedrock. Learners should have basic Python skills and a beginner-level understanding of LLMs. Both coders and non-technical professionals benefit by gaining hands-on experience and understanding real-world AI applications, enabling them to engage confidently with AI projects or supervise AI-driven initiatives. The course blends concise lectures, hands-on demos, and step-by-step projects to guide learners. It starts with foundational setup and API interaction, progresses to building chatbots and RAG applications, and concludes with data automation, safeguards, and troubleshooting for practical mastery. This video is based on Introduction to Generative AI with Amazon Bedrock, by Varad G. Varadarajan (ConnectedWorld Technology Solutions). Copyright © 2025 Varad G. Varadarajan (ConnectedWorld Technology Solutions) All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

  • Introduction
    • This module provides an overview of Generative AI, introduces course objectives, and guides learners through installing essential tools and libraries for working with AI projects on Amazon Bedrock.
  • Introducing Amazon Bedrock
    • This module provides an overview of Amazon Bedrock, its key features, and how it simplifies working with large language models. Learners will gain hands-on understanding of interacting with the Bedrock API through practical examples and demonstrations.
  • Conversational Chatbots and RAG
    • This module covers the design, implementation, and integration of conversational chatbots using Amazon Bedrock, along with the principles and application of Retrieval-Augmented Generation (RAG) to enhance AI interactions.
  • More Generative AI Use Cases
    • This module explores advanced applications of generative AI, including generating structured data, understanding AI agents, and creating multimodal content. Learners will gain practical skills in leveraging AI for diverse tasks and real-world scenarios.
  • Bedrock Data Automation (BDA)
    • This module introduces the core principles of Bedrock Data Automation, covering its role in streamlining AI workflows and improving efficiency. Learners will gain an understanding of how BDA can be applied in practical scenarios and explore real-world implementations through demonstrations.
  • Observability and Safeguards
    • This module explores how to implement observability and safeguards in AI applications, focusing on monitoring, reliability, and ethical constraints. Learners will gain practical skills in creating programmatic guardrails to ensure responsible AI behavior. The content emphasizes both technical and ethical aspects of AI system design.
  • Troubleshooting
    • This module equips learners with the skills to identify, analyze, and resolve common errors in generative AI applications. It provides practical problem-solving techniques and real-world examples to improve troubleshooting efficiency and effectiveness.
  • Conclusion
    • This module offers practical exercises to reinforce learning, reviews real-world applications of generative AI, and provides additional insights and resources for further exploration of Amazon Bedrock.

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