Amazon Bedrock & AWS Generative AI [Beginner to Advanced] | Udemy


Amazon Bedrock & AWS Generative AI [Beginner to Advanced] | Udemy [Update 07/2024]
English | Size: 6.62 GB
Genre: eLearning

Build a Chatbot, Banking Agent, Image Generation, RAG, Summarization with Amazon Bedrock. No prior 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 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 – Llama 2 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 : Code Generation using AWS CodeWhisperer and CDK – In Typescript
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

Amazon Bedrock and 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 – Llama 2, 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 – Code Generation using AWS CodeWhisperer and CDK – In Typescript

  • 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 – Llama 2, 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. Llama 2 Foundation Model
  3. Cohere Foundation Model
  4. Stability Diffusion Model
  5. Claude Foundation Model from Anthropic
  6. Claude Sonnet
  7. Amazon Bedrock Agents
  8. Bedrock Knowledge Base
  9. Langchain – Chains and Memory Modules
  10. FAISS Vector Store
  11. AWS Code Generation using AWS Code Whisperer
  12. API Gateway
  13. AWS Lambda
  14. AWS DynamoDB
  15. Open API Schema
  16. Streamlit
  17. S3
  18. Prompt design Techniques (Zero Shot, One Shot.)  for Claude, Titan and Stability AI Foundation Models (LLMs)
  19. Fine Tuning Foundation Models – Theory and Hands-On
  20. Python
  21. Evaluation of Foundation Models – Theory and Hands-On
  22. Basics of AI, ML, Artificial Neural Networks
  23. Basics of Generative AI
  24. 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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