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Generative AI on AWS - Amazon Bedrock, RAG & Langchain (2025) - OneDDL - 09-19-2025 Free Download Generative AI on AWS - Amazon Bedrock, RAG & Langchain (2025) Last updated 9/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 7.77 GB | Duration: 13h 9m Build 9+ GenAI Use Cases on AWS with Amazon Bedrock, RAG, Langchain, AI Agents, MCP, Amazon Q, LLM. No AI/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 Model Use Case 2: Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation Model Use Case 3 - Build a Chatbot using Bedrock Converse API - DeepSeek and Nova Pro Foundation Model, Langchain and Streamlit Use Case 4- Employee HR Q & A App with Retrieval Augmented Generation (RAG) - Bedrock - Claude Foundation Model + Langchain + FAISS + 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 & Knowledge Bases Use Case 7 - Build Infrastructure Coding Agent using Amazon Q CLI and AWS CloudFormation Server. Use Case 8 : Amazon Q Business - Build a Marketing Manager App with Amazon Q Use Case 9 - Capabilities of Amazon Q Developer over SDLC - HandsON Bedrock Logging with AWS CloudWatch GenAI Project Lifecycle: Phase 1 - Use Case Selection - Discuss about various phases of GenAI and How to identify right use case GenAI Project Lifecycle: Phase 2 - Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation Service GenAI Project Lifecycle: Phase 3 - Prompt Engineering - Factors Impacting Prompt design - Claude, Amazon Titan, Stability Diffusion, Prompt design Techniques GenAI Project Lifecycle: Phase 4 - Fine Tuning of Foundation Models - Theory and Hands-On Python Basics Refresher AWS Lambda and API Gateway Refresher Requirements 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. Only very very basic AWS knowledge such as what is S3, AWS Lambda etc. Description 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 - Build Infrastructure Coding Agent using Amazon Q CLI and AWS CloudFormation Server. Use Case 8 - Amazon Q Business - Build a Marketing Manager App with Amazon Q Business Use Case 9 - 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 Amazon Bedrock AWS CloudFormation MCP Server and Q CLI Deepseek and Nova Pro Foundation Model Cohere Foundation Model Stability Diffusion Model Claude Foundation Model from Anthropic Claude Sonnet Amazon Bedrock Agents Bedrock Knowledge Base Langchain - Chains and Memory Modules FAISS Vector Store AWS Code Generation using AWS Code Whisperer API Gateway AWS Lambda AWS DynamoDB Open API Schema Streamlit S3 Prompt design Techniques (Zero Shot, One Shot.) for Claude, Titan and Stability AI Foundation Models (LLMs) Fine Tuning Foundation Models - Theory and Hands-On Python Evaluation of Foundation Models - Theory and Hands-On Basics of AI, ML, Artificial Neural Networks Basics of Generative AI Everything related to AWS Amazon Bedrock Who this course is for The course is designed to help you switch careers and move into lucrative Generative AI and Amazon Bedrock roles. 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