Building Generative AI Applications Using Amazon Bedrock

This course is designed for data scientists and machine learning developers who are interested in building generative artificial intelligence (generative AI) applications using either the Amazon Bedrock API or LangChain integration. In this course, you will learn about the architecture patterns to build applications for key generative AI use cases.

The modules in this course prepare you to work through examples of generating and summarizing text, question answering, and a chatbot. The labs demonstrate the use of Amazon Bedrock models by using API calls, SDKs, and open source tools, such as LangChain.

•Course level: Advanced

•Duration: 4 hours

Activities

This course includes eLearning interactions, knowledge checks, and labs.

Course objectives

In this course, you will learn to:

•Identify the components of a generative AI application and how to customize a foundation model (FM)

•Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs

•Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications

•Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock

•Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications

•Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach

Intended audience

This course is intended for:

•Data scientists

•Machine learning (ML) developers

Prerequisites

We recommend that attendees of this course have:

•Intermediate to expert-level proficiency with Python programming language

•AWS Technical Essentials

•Practical Data Science with Amazon SageMaker (intermediate) 

•Amazon Bedrock Getting Started (Fundamental)

•Foundations of Prompt Engineering (Intermediate)

Course outline

Module 1: Introduction to Amazon Bedrock

•Building Generative AI Applications on Amazon Bedrock

•Applications and Use Cases

•Topics Covered in Future Modules

•Conclusion

Module 2: Application Components

•Overview of Generative AI Application Components

•Foundation Models and the FM Interface

•Working with Datasets and Embeddings

•Additional Application Components

•RAG

•Model Fine-Tuning

•Securing Generative AI Applications

•Generative AI Application Architecture

•Knowledge Check

•Conclusion

Module 3: Foundation Models

•Introduction to Amazon Bedrock Foundation Models

•Using Amazon Bedrock FMs for Inference

•Amazon Bedrock Methods

•Data Protection and Auditability

•Knowledge Check

•Conclusion

Module 4: Using LangChain

•Optimizing LLM Performance

•Integrating AWS and LangChain

•Using Models with LangChain

•Constructing Prompts

•Structuring Documents with Indexes

•Storing and Retrieving Data with Memory

•Using Chains to Sequence Components

•Managing External Resources with LangChain Agents

•Knowledge Check

•Conclusion

Module 5: Architecture Patterns

•Introduction to Architecture Patterns

•Test Generation and Text Summarization

•Question Answering

•Chatbots

•Code Generation

•LangChain and Amazon Bedrock Agents

•Knowledge Check

•Conclusion

Module 6: Hands-on Labs 

•Introduction to Labs

•Lab 1: Performing Text Generation

•Lab 2: Creating Text Summarization

•Lab 3: Using Amazon Bedrock for Question and Answering

•Lab 4: Building a Chatbot

•Lab 5: Using Amazon Bedrock Models for Code Generation

•Lab 6: Integrating Amazon Bedrock Models with LangChain Agents

•Conclusion