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

Overview

8+ Use Cases with Amazon Bedrock, Amazon Q, Agents, Knowledge Bases, Chatbot,LangChain,DeepSeek. No AI or Coding exp req What you'll learn:

Learn fundamentals about AI, Machine Learning and Artificial Neural Networks.Learn how Generative AI works and deep dive into Foundation Models.Amazon Bedrock – Detailed Console Walkthough, Bedrock Architecture, Pricing and Inference Parameters.Use Case 1:

Media and Entertainment Industry:

Generate Movie Poster Design using API Gateway, S3 and Stable Diffusion Foundation ModelUse Case 2:

Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation ModelUse Case 3 - Build a Chatbot using Bedrock Converse API - DeepSeek and Nova Pro Foundation Model, Langchain and StreamlitUse Case 4- Employee HR Q & A App with Retrieval Augmented Generation (RAG) - Bedrock - Claude Foundation Model + Langchain + FAISS + StreamlitUse Case 5 :

Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API GatewayUse Case 6 :

Build a Retail Banking Agent using Amazon Bedrock Agents & Knowledge BasesUse Case 7 :

Amazon Q Business - Build a Marketing Manager App with Amazon QUse Case 8 - Capabilities of Amazon Q Developer over SDLC - HandsONBedrock Logging with AWS CloudWatchGenAI Project Lifecycle:

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

Phase 2 - Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation ServiceGenAI Project Lifecycle:

Phase 3 - Prompt Engineering - Factors Impacting Prompt design - Claude, Amazon Titan, Stability Diffusion, Prompt design TechniquesGenAI Project Lifecycle:

Phase 4 - Fine Tuning of Foundation Models - Theory and Hands-OnPython Basics RefresherAWS Lambda and API Gateway Refresher 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 ModelUse Case 2 - Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation ModelUse 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)+ StreamlitUse Case 5 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API GatewayUse Case 6 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases - Claude Sonnet + AWS Lambda + DynamoDB + Bedrock Agents + Knowledge Bases + OpenAPI SchemaUse Case 7 - Amazon Q Business - Build a Marketing Manager App with Amazon Q BusinessUse Case 8 - Amazon Q Developer - Overview of the Code Generation capabilities of Amazon Q Developer - Over the SDLCWelcome 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 OverviewSection 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 ModelSection 6 - Use Case 2:

Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation ModelSection 7 - Use Case 3 :

Build a Chatbot using Bedrock - DeepSeek, Langchain and StreamlitSection 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)+ StreamlitSection 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, LambdaSection 11 - GenAI Project Lifecycle:

Phase 1 - Use Case Selection - Discuss about various phases of GenAI and How to identify right use caseSection 12 - GenAI Project Lifecycle:

Phase 2 - Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation ServiceSection 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 ModelsSection 14 - GenAI Project Lifecycle:

Phase 4 - Fine Tuning of Foundation Models - Theory and Hands-OnSection 15 - Code Generation using AWS CodeWhisperer and CDK - In TypescriptSection 16 - Python Basics RefresherSection 17 - AWS Lambda RefresherSection 18 - AWS API Gateway RefresherServices Used in the Course :

Amazon BedrockAmazon Q Deepseek and Nova Pro Foundation ModelCohere Foundation ModelStability Diffusion ModelClaude Foundation Model from AnthropicClaude SonnetAmazon Bedrock AgentsBedrock Knowledge BaseLangchain - Chains and Memory ModulesFAISS Vector StoreAWS Code Generation using AWS Code Whisperer API GatewayAWS LambdaAWS DynamoDBOpen API SchemaStreamlitS3Prompt design Techniques (Zero Shot, One Shot.) for Claude, Titan and Stability AI Foundation Models (LLMs)Fine Tuning Foundation Models - Theory and Hands-OnPythonEvaluation of Foundation Models - Theory and Hands-OnBasics of AI, ML, Artificial Neural NetworksBasics of Generative AIEverything related to AWS Amazon Bedrock

Syllabus

• Course Introduction
Overview of Amazon Bedrock, Amazon Q, and AWS Generative AI
Course objectives and outcomes
Evaluation methods and grading criteria
• Introduction to AI and Machine Learning
Basics of AI and ML concepts
Overview of generative AI
• Amazon Bedrock
Introduction to Amazon Bedrock
Benefits and features of using Bedrock
Setting up and configuring Bedrock for AI applications
Case studies and real-world applications
• Amazon Q
Understanding Amazon Q platform
Using Q for quantum computing integration
Key features and functionalities
Hands-on session: Deploying AI models with Amazon Q
• AWS Generative AI
Overview of AWS Generative AI services
Tools and APIs available for generative AI on AWS
Building and deploying generative models with AWS services
Best practices for using AWS GenAI efficiently
• Integration & Use Cases
Integrating Bedrock and Amazon Q with AWS AI services
Exploring industry use cases: Healthcare, Finance, and E-commerce
Real-world applications and success stories
• Security and Ethics
Security best practices for AI deployments
Ethical considerations in using AI technologies
Ensuring compliance with global standards
• Hands-On Labs and Workshops
Lab setup and necessary resources
Practical exercises and projects for skill building
Guide to creating a generative AI application
• Final Project and Presentation
Guidelines for final project submission
Tips for project success and evaluation criteria
Preparing and delivering effective presentations
• Course Conclusion
Summary of key learnings and skills acquired
Future trends in AI and roadmap for continuous learning
Course feedback and next steps

Taught by

Rahul Trisal

Subjects

Programming