AWS Amazon Bedrock & Generative AI - Beginner to Advanced from Udemy

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) -

Amazon Bedrock (Claude Foundation Model) + 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 - 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 - GenAI Project Lifecycle: Phase 1 - Use Case Selection - Discuss about various phases of GenAI and How to identify right use case
Section 11 - GenAI Project Lifecycle: Phase 2 - Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation Service
Section 12 - 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 13 - GenAI Project Lifecycle: Phase 4 - Fine Tuning of Foundation Models - Theory and Hands-On
Section 14 - Code Generation using AWS CodeWhisperer and CDK - In Typescript
Section 15 - Python Basics Refresher
Section 16 - AWS Lambda Refresher
Section 17 -) 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

What's inside

Learning objectives

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
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
Use case 6 : code generation using aws codewhisperer and cdk - in typescript
Python basics refresher
Aws lambda and api gateway refresher
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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
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
Use case 6 : code generation using aws codewhisperer and cdk - in typescript
Python basics refresher
Aws lambda and api gateway refresher
Show more
Show less

Syllabus

Introduction

Course Introduction

Please download the slides used in the lectures below
All the code and associated files are provided in the individual sections.

(Optional Section) Basics of AI, ML & Neural Networks - Overview for Beginners

Section Overview - Evolution of Generative AI

What is Artificial Intelligence ?

Machine Learning Overview - Supervised, Unsupervised and Reinforced Learning

Deep Learning and Neural Networks Overview

Knowledge Check - AI, ML & Neural Network

Generative AI & Foundation Models Concepts

Section Overview - Generative AI & Foundation Models Concepts

What is Generative AI and Use Cases

How Generative AI works 1 - Prompt, Completion and Infererences

How Generative AI works - Basic Concepts and Terminology - 2

Service Offerings in Generative AI from AWS

Amazon Bedrock – Deep Dive

Section Overview - Amazon Bedrock

Amazon Bedrock - Introduction

Bedrock - Console Walkthrough - 1

Bedrock - Console Walkthrough -2

Amazon Bedrock - Architecture

Amazon Bedrock - Infererence Parameters - Temperature

Amazon Bedrock - Infererence Parameters - 2

Bedrock - Pricing

Enterprise Use Case 1 (Hands-On) : Media and Entertainment Industry

Section Introduction- Use Case for Media and Entertainment Industry

Use Case Description - Media and Entertainment Industry

Use Case Architecture - Amazon Bedrock (Stability AI), Lambda and S3

Use Case Pre-Requisites - Model Access and Lambda Boto3 Version Upgrade

Creation of S3 Bucket and Lambda Function

AWS Lambda and Bedrock Integration - 1

AWS Lambda and Bedrock Integration - 2

Storing the Image generated by Bedrock in S3 Bucket

Generating a Pre Signed URL for Image in S3 Bucket

AWS API Gateway and Lambda Integration

End to End Demo

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

Section Introduction : Use Case 2 - Text Summarization

Text Summarization - Use Case Description and Architecture

Creation of AWS Lambda function and Boto3 upgrade

Writing the AWS Lambda function to connect to Bedrock Service - 1

Writing the AWS Lambda function to connect to Bedrock Service - 2

Create REST API using AWS API Gateway and Lambda Integration

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

Chatbot - Demo of what we will Build and Architecture

Chatbot - Environment Setup before coding

Chatbot Backend - 1

Chatbot Backend - 2

Chatbot Frontend

Chatbot - End to End Demo

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

HR Q & A (with RAG) - Demo of what we will Build

(Optional Lecture) - Overview of Vectors, Embedding, Vector DB and Search

HR Q & A (with RAG) - Architecture for the Use Case

RAG - Environment Setup before coding

HR Q&A (with RAG) - HandsOn - Data Load - Part 1

HR Q&A (with RAG) - HandsOn - Data Transform - Part 2

HR Q&A (with RAG) - HandsOn - Embedding, Vector Store & Index - Part 3

HR Q&A (with RAG) - HandsOn - LLM Creation + Context - Part 4

HR Q&A (with RAG) - HandsOn - Frontend and Final Demo

HR Q&A (with RAG) - End to End Demo

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

Demo of what we will Build - Amazon Bedrock Knowledge Base, Lambda, API Gateway

What is Bedrock Knowledge Base - Concept and Architecture

Creation of Amazon Bedrock Knowledge Base

Retrieve API and RetrieveAndGenerate API for data retrieval - Concept

Knowledge Base and AWS Lambda Creation - Part 1

Knowledge Base and AWS Lambda Creation - Boto3 upgrade - Part 2

Knowledge Base and AWS Lambda Creation - Part 3

Knowledge Base - REST API creation and Lambda Integration

Knowledge Base - Clean up (To avoid charges)

Phase 1 of GenAI Project - Use Case Identification

Section Overview - GenAI Project Lifecycle and Use Case Identification

Overview of GenAI Project Lifecycle

GenAI - Use Case Identification Approach

Phase 2 of GenAI Project - Foundation Model Selection

Section Overview - Foundation Model Selection for your Use Case

Foundation Model Selection Criteria - Theory - Part 1

Foundation Model Selection Criteria - Theory - Part 2

Foundation Model Selection Criteria - HandsOn

Phase 3 of GenAI Project - Prompt Engineering

Section Overview - Prompt Engineering

Prompt Engineering - 1

Prompt Engineering - 2

Prompt Engineering Techinques

Steps to engineer a good prompt

Phase 3 of GenAI Project - Fine Tuning of Foundation Model

Section Overview - Fine Tuning of Foundation Model

Fine Tuning of Foundation Model - Overview

Fine Tuning of Foundation Model - Architecture

Amazon Bedrock - Data Privacy Challenges

Fine Tuning of Foundation Models - Hands On

Use Case 6-Code Generation with AWS CodeWhisperer (From my AWS CDK Udemy Course)

Amazon CodeWhisperer - Introduction

Amazon CodeWhisperer - Installation

Amazon CodeWhisperer - Create S3 bucket

Amazon CodeWhisperer - Create VPC and RDS

Python Basics Refresher

Basic Python Programming Refresher - Part 1

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for learners with basic AWS knowledge, providing a refresher on Python, AWS Lambda, and API Gateway

Appropriate for beginners, offering a starting point for understanding AI, machine learning, and artificial neural networks

Provides hands-on experience through use cases, allowing learners to apply concepts practically

Covers advanced topics such as prompt engineering, fine-tuning of foundation models, and code generation, appealing to intermediate and advanced learners

Career center

Learners who complete AWS Amazon Bedrock & Generative AI - Beginner to Advanced will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist collects, analyzes, and interprets data to extract meaningful insights. This course provides a strong foundation in AI, machine learning, and generative AI, which are essential skills for a Data Scientist. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Data Scientists.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer develops, tests, and deploys machine learning models to solve real-world problems. Machine learning is a subfield of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. This course provides a strong foundation in AI, machine learning, and generative AI, which are essential skills for a Machine Learning Engineer. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

A Business Analyst analyzes business needs and develops solutions to improve business processes. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used to improve business processes. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Business Analysts.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical models to improve the efficiency of business operations. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in operations research. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Operations Research Analysts.

See salaries and explore the career path for Operations Research Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in software development. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Software Engineers.

See salaries and explore the career path for Software Engineer

Financial Analyst

A Financial Analyst analyzes financial data to make investment recommendations. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in financial analysis. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Financial Analysts.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

A Marketing Analyst analyzes marketing data to improve marketing campaigns. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in marketing analytics. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Marketing Analysts.

See salaries and explore the career path for Marketing Analyst

Risk Analyst

A Risk Analyst identifies and assesses risks to a business. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used to identify and assess risks. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Risk Analysts.

See salaries and explore the career path for Risk Analyst

Product Manager

A Product Manager is responsible for the development and launch of new products. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used to develop new products. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Product Managers.

See salaries and explore the career path for Product Manager

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in quantitative analysis. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Quantitative Analysts.

See salaries and explore the career path for Quantitative Analyst

Actuary

An Actuary uses mathematical and statistical models to assess and manage risk. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in actuarial science. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Actuaries.

See salaries and explore the career path for Actuary

Technical Writer

A Technical Writer creates and maintains technical documentation. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used to create and maintain technical documentation. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Technical Writers.

See salaries and explore the career path for Technical Writer

Customer Success Manager

A Customer Success Manager ensures that customers are satisfied with a company's products or services. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in customer success management. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Customer Success Managers.

See salaries and explore the career path for Customer Success Manager

Market Researcher

A Market Researcher conducts research to understand market trends and consumer behavior. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in market research. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Market Researchers.

See salaries and explore the career path for Market Researcher

Sales Analyst

A Sales Analyst analyzes sales data to improve sales strategies. This course provides a strong foundation in AI, machine learning, and generative AI, which are increasingly being used in sales analytics. The course also covers Amazon Bedrock, a platform for developing and deploying generative AI models, which is a valuable tool for Sales Analysts.

See salaries and explore the career path for Sales Analyst