Amazon Bedrock, Amazon Q & AWS Generative AI [2025]

Amazon Bedrock, Amazon Q and AWS GenAI Course :

***Hands - On Use Cases implemented as part of this course***

Use Case 1 - Generate Poster Design for Media Industry using  API Gateway, S3 and Stable Diffusion Foundation Model

Use Case 2 - Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model

Use Case 3 - Build a Chatbot using Amazon Bedrock - DeepSeek, Langchain and Streamlit.

Use Case 4- Build an Employee HR Q & A Application with Retrieval Augmented Generation (RAG) -

                      Claude FM + Langchain (Ochestrator)+ FAISS (Vector DB) + Streamlit

Use Case 5 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API Gateway

Use Case 6 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases -

                          Claude Sonnet + AWS Lambda + DynamoDB + Bedrock Agents + Knowledge Bases + OpenAPI Schema

Use Case 7 - Amazon Q Business - Build a Marketing Manager App with Amazon Q Business

Use Case 8 - Amazon Q Developer - Overview of the Code Generation capabilities of Amazon Q Developer - Over the SDLC

• Welcome to the most comprehensive guide on Amazon Bedrock and Generative AI on AWS from a practising AWS Solution Architect and best-selling Udemy Instructor.

• This course will start from absolute basics on AI/ML, Generative AI and Amazon Bedrock and teach you how to build end to end enterprise apps on Image Generation using Stability Diffusion Foundation, Text Summarization using Cohere, Chatbot using Llama 2,Langchain, Streamlit and Code Generation using Amazon CodeWhisperer.

• The focus of this course is to help you switch careers and move into lucrative Generative AI roles.

• There are no course pre-requisites for this course except basic AWS Knowledge. I will provide basic overview of AI/ML concepts and have included Python, AWS Lambda and API Gateway refresher at end of course in case you are not familiar with python coding or these AWS services.

• I will continue to update this course as the GenAI and Bedrock evolves to give you a detailed understanding and learning required in enterprise context, so that you are ready to switch careers.

  
  

  Detailed Course Overview

• Section 2 - Evolution of Generative AI: Learn fundamentals about AI, Machine Learning and Artificial Neural Networks (Layers, Weights & Bias).

• Section 3 - Generative AI & Foundation Models Concepts: Learn about How Generative AI works (Prompt, Inference, Completion, Context Window etc.) & Detailed Walkthrough of Foundation Model working.

• Section 4 - Amazon Bedrock – Deep Dive: Do detailed Console Walkthough, Bedrock Architecture, Pricing and Inference Parameters.

• Section 5 - Use Case 1: Media and Entertainment Industry: Generate Movie Poster Design using API Gateway, S3 and Stable Diffusion Foundation Model

• Section 6 - Use Case 2: Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model

• Section 7 - Use Case 3 : Build a Chatbot using Bedrock - DeepSeek, Langchain and Streamlit

• Section 8 - Use Case 4- Build a Employee HR Q & A Application with Retrieval Augmented Generation (RAG) -

                          Amazon Bedrock (Claude Foundation Model) + Langchain (Ochestrator)+ FAISS (Vector DB) + Streamlit

• Section 9 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API Gateway

• Section 10 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases, Dynam0DB, Lambda

• Section 11 - GenAI Project Lifecycle: Phase 1 - Use Case Selection - Discuss about various phases of GenAI and How to identify right use case

• Section 12 - GenAI Project Lifecycle: Phase 2 - Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation Service

• Section 13 - GenAI Project Lifecycle: Phase 3 - Prompt Engineering - Factors Impacting Prompt design, Prompt design Techniques (Zero Shot, One Shot.), Good practices for writing prompts for Claude, Titan and Stability AI Foundation Models

• Section 14 - GenAI Project Lifecycle: Phase 4 - Fine Tuning of Foundation Models - Theory and Hands-On

• Section 15 - Code Generation using AWS CodeWhisperer and CDK - In Typescript

• Section 16 - Python Basics Refresher

• Section 17 - AWS Lambda Refresher

• Section 18 - AWS API Gateway Refresher

Services Used in the Course :

1. Amazon Bedrock

2. Amazon Q

3. Deepseek and Nova Pro Foundation Model

4. Cohere Foundation Model

5. Stability Diffusion Model

6. Claude Foundation Model from Anthropic

7. Claude Sonnet

8. Amazon Bedrock Agents

9. Bedrock Knowledge Base

10. Langchain - Chains and Memory Modules

11. FAISS Vector Store

12. AWS Code Generation using AWS Code Whisperer

13. API Gateway

14. AWS Lambda

15. AWS DynamoDB

16. Open API Schema

17. Streamlit

18. S3

19. Prompt design Techniques (Zero Shot, One Shot.)  for Claude, Titan and Stability AI Foundation Models (LLMs)

20. Fine Tuning Foundation Models - Theory and Hands-On

21. Python

22. Evaluation of Foundation Models - Theory and Hands-On

23. Basics of AI, ML, Artificial Neural Networks

24. Basics of Generative AI

25. Everything related to AWS Amazon Bedrock