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Generative AI with Amazon Bedrock

You're reading from Generative AI with Amazon Bedrock Build, scale, and secure generative AI applications using Amazon Bedrock

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781803247281

Length 384 pages

Edition 1st Edition

Tools

AWS

Concepts

Artificial Intelligence

Authors (2):

Shikhar Kwatra

Bunny Kaushik

View More author details

Table of Contents (18) Chapters

Preface

1. Part 1: Amazon Bedrock Foundations FREE CHAPTER

2. Chapter 1: Exploring Amazon Bedrock

3. Chapter 2: Accessing and Utilizing Models in Amazon Bedrock

4. Chapter 3: Engineering Prompts for Effective Model Usage

5. Chapter 4: Customizing Models for Enhanced Performance

6. Chapter 5: Harnessing the Power of RAG

7. Part 2: Amazon Bedrock Architecture Patterns

8. Chapter 6: Generating and Summarizing Text with Amazon Bedrock

9. Chapter 7: Building Question Answering Systems and Conversational Interfaces

10. Chapter 8: Extracting Entities and Generating Code with Amazon Bedrock

11. Chapter 9: Generating and Transforming Images Using Amazon Bedrock

12. Chapter 10: Developing Intelligent Agents with Amazon Bedrock

13. Part 3: Model Management and Security Considerations

14. Chapter 11: Evaluating and Monitoring Models with Amazon Bedrock

15. Chapter 12: Ensuring Security and Privacy in Amazon Bedrock

16. Index

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17. Other Books You May Enjoy

Technical requirements

This chapter requires you to have access to an AWS account. If you don’t have one already, you can go to https://aws.amazon.com/getting-started/ and create one.

Secondly, you will need to install and configure the AWS CLI (https://aws.amazon.com/cli/). You will use this to access Amazon Bedrock FMs from your local machine. Since the majority of the code cells we will be executing are based in Python, setting up an AWS Python SDK (Boto3) (https://docs.aws.amazon.com/bedrock/latest/APIReference/welcome.html) would be beneficial at this point. You can carry out the Python setup in any way: install it on your local machine, or use AWS Cloud9, or utilize AWS Lambda, or leverage Amazon SageMaker. If you’re using Jupyter Notebook with the AWS Python SDK to interact with Amazon Bedrock, make sure you run the following code cell in the notebook to import the essential libraries and create a Bedrock runtime client:

#Ensure you have the latest version...

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Authors (2)

Shikhar Kwatra

Shikhar Kwatra

Shikhar Kwatra, a senior AI/ML solutions architect at Amazon Web Services, holds the distinction of being the world's Youngest Master Inventor with over 500 patents in AI/ML and IoT domains. He serves as a technical journal reviewer, book author, and educator. Shikhar earned his Master's in Electrical Engineering from Columbia University. With over a decade of experience spanning startups to large-scale enterprises, he specializes in architecting scalable, cost-efficient cloud environments and supports GSI partners in developing strategic industry solutions. Beyond his professional pursuits, Shikhar finds joy in playing the guitar, composing music, and practicing mindfulness.

See other products by Shikhar Kwatra

Bunny Kaushik

Bunny Kaushik

Bunny Kaushik is an AWS solution architect and ML specialist who loves to build solutions and help customers innovate on the AWS platform. He is an Amazon SageMaker SME, generative AI hero, and ML thought leader within AWS. He has over 10 years of experience working as an ML specialist and managing projects across different teams and organizations. Outside of work, he enjoys swimming, playing volleyball, rock climbing, and exploring new places.

See other products by Bunny Kaushik

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