babymore87
MyBoerse.bz Pro Member
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 + FAISSÂ + 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 â 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.
Homepage
Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
No Password - Links are Interchangeable
