Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Generative AI with Amazon Bedrock

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

Arrow left icon
Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781803247281
Length 384 pages
Edition 1st Edition
Tools
Arrow right icon
Authors (2):
Arrow left icon
Shikhar Kwatra Shikhar Kwatra
Author Profile Icon Shikhar Kwatra
Shikhar Kwatra
Bunny Kaushik Bunny Kaushik
Author Profile Icon Bunny Kaushik
Bunny Kaushik
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

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 17. Other Books You May Enjoy

Understanding the generative AI landscape

Since the advent of ChatGPT, organizations across the globe have explored a plethora of use cases that generative AI can solve for them. They have built several innovation teams and teams of data scientists to build and explore various use cases, including summarizing long documents, extracting information from documents, and performing sentiment analysis to gauge satisfaction or discontent toward a product or service. If you have been working in the machine learning (ML) or natural language processing (NLP) field, you may be familiar with how a language model works – by understanding the relationship between the words in documents. The main objective of these language models is to predict the next probable word in a sentence.

If you look at the sentence John loves to eat, a natural language model is trying to predict what the next word or token in the sequence will be. Here, the next probable word seems to be ice-cream, with a 9...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime