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

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Amazon Bedrock & Generative AI - Beginner to Advanced with these activities:

Review knowledge of Stable Diffusion

Show steps

Review the fundamental concepts of Stable Diffusion, including how it generates images from text prompts.

Browse courses on Generative AI

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Read the Stable Diffusion documentation.
Watch tutorials and videos about using Stable Diffusion.
Practice using Stable Diffusion to generate images.

Review Python Basics

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Improve your Python proficiency before the course begins by reviewing basic syntax, data types, and control flow.

Browse courses on Python

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Review online tutorials or documentation on Python basics.
Practice writing simple Python programs using an online IDE or your local environment.

Find mentors who can provide guidance on Generative AI

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Connect with experts in the field of Generative AI who can provide guidance and support.

Browse courses on Generative AI

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Attend industry events and meetups.
Join online communities and forums.
Reach out to professors and researchers in the field.
Contact companies that are using Generative AI.

Ten other activities

Expand to see all activities and additional details

Show all 13 activities

Follow tutorials on using the Amazon Bedrock API

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Become familiar with the Amazon Bedrock API and how to use it to generate text, images, and other types of content.

Browse courses on Amazon Bedrock

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Find tutorials on the Amazon Bedrock website or other online resources.
Follow the tutorials step-by-step.
Experiment with the API to learn how it works.

Practice writing prompts for AI models

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Writing prompts is a crucial skill for working with AI models. Practice writing prompts that are specific, relevant, and clear.

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Identify the purpose of the AI model and the desired output.
Brainstorm a list of keywords and concepts related to the topic.
Craft a concise and specific prompt that includes the necessary details.
Review and refine the prompt to ensure clarity and effectiveness.

Practice using Python to interact with the Amazon Bedrock API

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Gain proficiency in using Python to interact with the Amazon Bedrock API.

Browse courses on Python

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Write Python code to interact with the API.
Set up a Python development environment.
Install the Amazon Bedrock API client library.
Test your code.

Complete Coding Challenges on LeetCode

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Sharpen your coding skills and prepare for GenAI project implementation by solving coding challenges on LeetCode.

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Select problems from LeetCode that align with the GenAI concepts covered in the course.
Attempt to solve the problems on your own, referring to the problem description and discussion forums for assistance.
Review solutions and explanations provided by the LeetCode community to enhance your understanding.

Join a Study Group for GenAI and Bedrock

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Enhance your understanding of GenAI and Bedrock by collaborating with peers in a study group.

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Reach out to your classmates or join online communities to find other students interested in forming a study group.
Establish regular meeting times and decide on the topics to cover each session.
Take turns presenting concepts, discussing use cases, and working on exercises together.

Build a project using Amazon Bedrock - Image generation

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Apply your knowledge of Amazon Bedrock by building a project that uses the API to generate images.

Browse courses on Amazon Bedrock

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Choose a project idea.
Design the project architecture.
Implement the project using the Amazon Bedrock API.
Test and deploy the project.

Build a Simple Chatbot Using Bedrock

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Gain hands-on experience with Bedrock by building a basic chatbot that interacts with users and responds based on prompts.

Browse courses on Chatbot Development

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Set up your AWS account and create an Amazon Bedrock instance.
Design a simple chatbot interface using a tool like Streamlit or Flask.
Integrate your chatbot with Bedrock using the AWS SDK for Python or Boto3.
Test your chatbot and iterate on its responses to improve its performance.

Develop a GenAI Project Proposal

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Solidify your understanding of GenAI and its applications by developing a detailed project proposal that outlines a specific use case and implementation plan.

Browse courses on Proposal Writing

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Identify a real-world problem or opportunity that can be addressed using GenAI.
Research and select a GenAI model that is suitable for your project.
Develop a project plan that includes a timeline, resource requirements, and evaluation metrics.
Write a project proposal document that clearly articulates your project goals, approach, and expected outcomes.

Attend a GenAI Hands-on Workshop

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Deepen your understanding of GenAI and Bedrock by attending a hands-on workshop led by industry experts.

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Search for GenAI workshops offered by AWS, universities, or industry organizations.
Register for a workshop that aligns with your interests and learning goals.
Attend the workshop, actively participate in the exercises, and engage with the instructors.
Follow up after the workshop by reviewing the materials, practicing the techniques, and connecting with other attendees.

Participate in a GenAI Hackathon

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Challenge yourself and test your GenAI skills by participating in a hackathon where you can collaborate with others to build innovative solutions.

Browse courses on Problem Solving

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Find a GenAI hackathon that aligns with your interests and skills.
Form a team or join an existing one with complementary skills.
Brainstorm ideas and develop a project proposal that showcases your GenAI knowledge and creativity.
Build and present your solution within the hackathon timeframe, addressing the challenges and demonstrating the potential of GenAI.

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