Amazon Bedrock AWS Generative AI Beginner to Advanced

Amazon Bedrock & AWS Generative AI – Beginner to Advanced, available at $24.99, has an average rating of 4.63, with 103 lectures, 1 quizzes, based on 1196 reviews, and has 9474 subscribers.

You will learn about 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 This course is ideal for individuals who are The course is designed to help you switch careers and move into lucrative Generative AI and Amazon Bedrock roles. It is particularly useful for The course is designed to help you switch careers and move into lucrative Generative AI and Amazon Bedrock roles.

Enroll now: Amazon Bedrock & AWS Generative AI – Beginner to Advanced

Summary

Title: Amazon Bedrock & AWS Generative AI – Beginner to Advanced

Price: $24.99

Average Rating: 4.63

Number of Lectures: 103

Number of Quizzes: 1

Number of Published Lectures: 101

Number of Published Quizzes: 1

Number of Curriculum Items: 104

Number of Published Curriculum Objects: 102

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

The course is designed to help you switch careers and move into lucrative Generative AI and Amazon Bedrock roles.

Target Audiences

The course is designed to help you switch careers and move into lucrative Generative AI and Amazon Bedrock roles.

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 Agentusing 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 Agentusing 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 GatewayRefresher

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: Tips to Optimize Learning & Download Course Content Slides

Chapter 2: Basics of AI, ML & Neural Networks – Overview for Beginners

Lecture 1: Section Overview – Evolution of Generative AI

Lecture 2: What is Artificial Intelligence ?

Lecture 3: Machine Learning Overview – Supervised, Unsupervised and Reinforced Learning

Lecture 4: Deep Learning and Neural Networks Overview

Chapter 3: Generative AI & Foundation Models Concepts

Lecture 1: Section Overview – Generative AI & Foundation Models Concepts

Lecture 2: What is Generative AI and Use Cases

Lecture 3: How Generative AI works 1 – Prompt, Completion and Infererences

Lecture 4: How Generative AI works – Basic Concepts and Terminology – 2

Lecture 5: Foundation Model vs Large Language Model vs Diffusion Model vs MultiModal

Lecture 6: Service Offerings in Generative AI from AWS

Chapter 4: Amazon Bedrock – Deep Dive

Lecture 1: Section Overview – Amazon Bedrock

Lecture 2: Amazon Bedrock – Introduction

Lecture 3: Bedrock – Console Walkthrough – 1

Lecture 4: Bedrock – Console Walkthrough -2

Lecture 5: Amazon Bedrock – Architecture

Lecture 6: Amazon Bedrock – Infererence Parameters – Temperature

Lecture 7: Amazon Bedrock – Infererence Parameters – 2

Lecture 8: Important – Course Rating and Feedback

Lecture 9: Bedrock – Pricing

Chapter 5: Enterprise Use Case 1 (Hands-On) : Image Generation – Bedrock, API GW, S3

Lecture 1: Section Introduction- Use Case for Media and Entertainment Industry

Lecture 2: Use Case Description – Media and Entertainment Industry

Lecture 3: Use Case Architecture – Amazon Bedrock (Stability AI), Lambda and S3

Lecture 4: Use Case Pre-Requisites – Model Access and Lambda Boto3 Version Upgrade

Lecture 5: Optional Lecture – Cost to implement the Use Cases if you follow along

Lecture 6: Creation of S3 Bucket and Lambda Function

Lecture 7: AWS Lambda and Bedrock Integration – 1

Lecture 8: AWS Lambda and Bedrock Integration – 2

Lecture 9: Storing the Image generated by Bedrock in S3 Bucket

Lecture 10: Generating a Pre Signed URL for Image in S3 Bucket

Lecture 11: AWS API Gateway and Lambda Integration

Lecture 12: End to End Demo

Chapter 6: Enterprise Use Case 2 (Hands-On) : Text Summarization- Bedrock, API GW, Lambda

Lecture 1: Section Introduction : Use Case 2 – Text Summarization

Lecture 2: Text Summarization – Use Case Description and Architecture

Lecture 3: Creation of AWS Lambda Function

Lecture 4: Writing the AWS Lambda function to connect to Bedrock Service – 1

Lecture 5: Writing the AWS Lambda function to connect to Bedrock Service – 2

Lecture 6: Create REST API using AWS API Gateway and Lambda Integration

Lecture 7: End to End Demo

Chapter 7: Use Case 3 (Hands-On) : Building a Chatbot with Llama 2, Langchain and Streamlit

Lecture 1: Chatbot – Demo of what we will Build and Architecture

Lecture 2: Chatbot – Environment Setup before coding

Lecture 3: Chatbot Backend – 1

Lecture 4: Chatbot Backend – 2

Lecture 5: Chatbot Frontend

Lecture 6: Chatbot – End to End Demo

Chapter 8: Use Case 4 (Hands-On) : Building HR Q & A with Retrieval Augmented Generation

Lecture 1: HR Q & A (with RAG) – Demo of what we will Build

Lecture 2: (Optional Lecture) – Overview of Vectors, Embedding, Vector DB and Search

Lecture 3: HR Q & A (with RAG) – Architecture for the Use Case

Lecture 4: RAG – Environment Setup before coding

Lecture 5: HR Q&A (with RAG) – HandsOn – Data Load – Part 1

Lecture 6: HR Q&A (with RAG) – HandsOn – Data Transform – Part 2

Lecture 7: HR Q&A (with RAG) – HandsOn – Embedding, Vector Store & Index – Part 3

Lecture 8: HR Q&A (with RAG) – HandsOn – LLM Creation + Context – Part 4

Lecture 9: HR Q&A (with RAG) – HandsOn – Frontend and Final Demo

Lecture 10: HR Q&A (with RAG) – End to End Demo

Chapter 9: Use Case 5 : Serverless E-Learning App with Knowledge Base, Lambda and API GW

Lecture 1: Demo of what we will Build – Amazon Bedrock Knowledge Base, Lambda, API Gateway

Lecture 2: What is Bedrock Knowledge Base – Concept and Architecture

Lecture 3: Creation of Amazon Bedrock Knowledge Base

Lecture 4: Retrieve API and RetrieveAndGenerate API for data retrieval – Concept

Lecture 5: Knowledge Base and AWS Lambda Creation – Part 1

Lecture 6: Knowledge Base and AWS Lambda Creation – Boto3 upgrade – Part 2

Lecture 7: Knowledge Base and AWS Lambda Creation – Part 3

Lecture 8: Knowledge Base – REST API creation and Lambda Integration

Lecture 9: Knowledge Base – Clean up (To avoid charges)

Chapter 10: Use Case 6 – Building a Retail Bank Agent using Bedrock Agents and KnowledgeBase

Lecture 1: Demo of what we will Build – Amazon Bedrock Agent

Lecture 2: Amazon Bedrock Agent Use Case – Architecture

Lecture 3: Retail Bank Agent – DynamoDB creation

Lecture 4: Retail Bank Agent – AWS Lambda Creation

Lecture 5: Retail Bank Agent – OpenAPI Specification document creation

Lecture 6: Bedrock Agent creation

Lecture 7: Bedrock Agent Permission to Invoke Lambda Function

Lecture 8: Integration – Bedrock Agent, Lambda and DynamoDB

Lecture 9: Bedrock Agent and KnowledgeBase integration

Chapter 11: Phase 1 of GenAI Project – Use Case Identification

Lecture 1: Section Overview – GenAI Project Lifecycle and Use Case Identification

Lecture 2: Overview of GenAI Project Lifecycle

Lecture 3: GenAI – Use Case Identification Approach

Chapter 12: Phase 2 of GenAI Project – Foundation Model Selection

Lecture 1: Section Overview – Foundation Model Selection for your Use Case

Lecture 2: Foundation Model Selection Criteria – Theory – Part 1

Lecture 3: Foundation Model Selection Criteria – Theory – Part 2

Lecture 4: Foundation Model Selection Criteria – HandsOn

Chapter 13: Phase 3 (A) of GenAI Project – Prompt Engineering

Lecture 1: Section Overview – Prompt Engineering

Lecture 2: Prompt Engineering – 1

Lecture 3: Prompt Engineering – 2

Lecture 4: Prompt Engineering Techinques

Lecture 5: Steps to engineer a good prompt

Instructors

Rahul Trisal
AWS Community Builder||7X Certified||AWS SA Professional

Rating Distribution

1 stars: 7 votes

2 stars: 6 votes

3 stars: 72 votes

4 stars: 344 votes

5 stars: 767 votes

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