Amazon Bedrock & AWS Generative AI – Beginner to Advanced

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