AWS Amazon Bedrock & Generative AI - Beginner to Advanced

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Rahul Trisal

9:32:45

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1 - Introduction

1 - Course Introduction.mp4

05:04

2 - Amazon-Bedrock-and-AWS-Generative-AI-Beginner-to-Advanced-v1.3-16022024.pdf

2 - Amazon-Bedrock-and-AWS-Generative-AI-Beginner-to-Advanced-v1.4-13032024.pdf

2 - Must Watch Tips to Optimize Learning Download Course Content Slides.mp4

02:36

2 - Optional Section Basics of AI ML Neural Networks Overview for Beginners

3 - Section Overview Evolution of Generative AI.mp4

01:03

4 - What is Artificial Intelligence.mp4

02:40

5 - Machine Learning Overview Supervised Unsupervised and Reinforced Learning.mp4

08:01

6 - Deep Learning and Neural Networks Overview.mp4

09:14

3 - Generative AI Foundation Models Concepts

7 - Section Overview Generative AI Foundation Models Concepts.mp4

00:49

8 - What is Generative AI and Use Cases.mp4

02:45

9 - How Generative AI works 1 Prompt Completion and Infererences.mp4

06:52

10 - How Generative AI works Basic Concepts and Terminology 2.mp4

07:56

11 - Service Offerings in Generative AI from AWS.mp4

06:50

4 - Amazon Bedrock Deep Dive

12 - Section Overview Amazon Bedrock.mp4

00:42

13 - Amazon Bedrock Introduction.mp4

04:56

14 - Bedrock Console Walkthrough 1.mp4

08:13

15 - Bedrock Console Walkthrough 2.mp4

09:05

16 - Amazon Bedrock Architecture.mp4

04:41

17 - Amazon Bedrock Infererence Parameters Temperature.mp4

09:16

18 - Amazon Bedrock Infererence Parameters 2.mp4

06:40

19 - Bedrock Pricing.mp4

06:48

5 - Enterprise Use Case 1 HandsOn Media and Entertainment Industry

20 - Section Introduction Use Case for Media and Entertainment Industry.mp4

01:13

21 - Use Case Description Media and Entertainment Industry.mp4

02:02

22 - Use Case Architecture Amazon Bedrock Stability AI Lambda and S3.mp4

02:09

23 - Boto3-Upgrade-Commands.pdf

23 - Use Case PreRequisites Model Access and Lambda Boto3 Version Upgrade.mp4

08:15

24 - Creation of S3 Bucket and Lambda Function.mp4

07:18

25 - AWS Lambda and Bedrock Integration 1.mp4

17:02

25 - pythoncode-lambda-movieposterdesignfunction.zip

26 - AWS Lambda and Bedrock Integration 2.mp4

08:13

27 - Storing the Image generated by Bedrock in S3 Bucket.mp4

09:56

28 - Generating a Pre Signed URL for Image in S3 Bucket.mp4

07:36

29 - AWS API Gateway and Lambda Integration.mp4

09:44

30 - End to End Demo.mp4

03:21

6 - Enterprise Use Case 2 HandsOn Text Summarization Bedrock API GW Lambda

31 - Section Introduction Use Case 2 Text Summarization.mp4

00:43

32 - Text Summarization Use Case Description and Architecture.mp4

02:32

33 - Creation of AWS Lambda function and Boto3 upgrade.mp4

02:35

34 - Writing the AWS Lambda function to connect to Bedrock Service 1.mp4

11:34

34 - pythoncode-lambda-demomanufacturing.zip

35 - Writing the AWS Lambda function to connect to Bedrock Service 2.mp4

16:37

35 - pythoncode-lambda-demomanufacturing.zip

36 - Create REST API using AWS API Gateway and Lambda Integration.mp4

05:48

37 - End to End Demo.mp4

03:12

7 - Use Case 3 HandsOn Building a Chatbot with Llama 2 Langchain and Streamlit

38 - Chatbot Demo of what we will Build and Architecture.mp4

05:59

39 - Chatbot Environment Setup before coding.mp4

13:13

40 - Chatbot Backend 1.mp4

14:05

40 - Chatbot-code.zip

41 - Chatbot Backend 2.mp4

08:15

42 - Chatbot Frontend.mp4

06:19

43 - Chatbot End to End Demo.mp4

01:29

8 - Use Case 4 HandsOn Building HR Q A with Retrieval Augmented Generation

44 - HR Q A with RAG Demo of what we will Build.mp4

02:58

45 - Optional Lecture Overview of Vectors Embedding Vector DB and Search.mp4

15:28

46 - HR Q A with RAG Architecture for the Use Case.mp4

07:42

47 - RAG Environment Setup before coding.mp4

02:34

47 - RAG-Install.pdf

48 - HR QA with RAG HandsOn Data Load Part 1.mp4

08:25

48 - data-load-test.zip

48 - rag-backend.zip

49 - HR QA with RAG HandsOn Data Transform Part 2.mp4

08:23

49 - data-split-test.zip

49 - rag-backend.zip

50 - HR QA with RAG HandsOn Embedding Vector Store Index Part 3.mp4

10:22

50 - rag-backend.zip

51 - HR QA with RAG HandsOn LLM Creation Context Part 4.mp4

07:34

51 - rag-backend.zip

52 - HR QA with RAG HandsOn Frontend and Final Demo.mp4

06:02

52 - rag-frontend.zip

53 - HR QA with RAG End to End Demo.mp4

01:25

9 - Use Case 5 Serverless ELearning App with Knowledge Base Lambda and API GW

54 - Demo of what we will Build Amazon Bedrock Knowledge Base Lambda API Gateway.mp4

06:07

55 - What is Bedrock Knowledge Base Concept and Architecture.mp4

05:09

56 - Creation of Amazon Bedrock Knowledge Base.mp4

10:44

57 - Retrieve API and RetrieveAndGenerate API for data retrieval Concept.mp4

04:50

58 - Knowledge Base and AWS Lambda Creation Part 1.mp4

16:01

59 - Knowledge Base and AWS Lambda Creation Boto3 upgrade Part 2.mp4

02:59

60 - Knowledge Base and AWS Lambda Creation Part 3.mp4

10:49

61 - Knowledge Base REST API creation and Lambda Integration.mp4

06:20

62 - Knowledge Base Clean up To avoid charges.mp4

01:33

10 - Phase 1 of GenAI Project Use Case Identification

63 - Section Overview GenAI Project Lifecycle and Use Case Identification.mp4

00:31

64 - Overview of GenAI Project Lifecycle.mp4

02:40

65 - GenAI Use Case Identification Approach.mp4

02:47

11 - Phase 2 of GenAI Project Foundation Model Selection

66 - Section Overview Foundation Model Selection for your Use Case.mp4

00:43

67 - Foundation Model Selection Criteria Theory Part 1.mp4

04:24

68 - Foundation Model Selection Criteria Theory Part 2.mp4

06:59

69 - Foundation Model Selection Criteria HandsOn.mp4

11:05

12 - Phase 3 of GenAI Project Prompt Engineering

70 - Section Overview Prompt Engineering.mp4

00:48

71 - Prompt Engineering 1.mp4

05:46

72 - Prompt Engineering 2.mp4

07:22

73 - Prompt Engineering Techinques.mp4

04:34

74 - Steps to engineer a good prompt.mp4

01:13

13 - Phase 3 of GenAI Project Fine Tuning of Foundation Model

75 - Section Overview Fine Tuning of Foundation Model.mp4

00:40

76 - Fine Tuning of Foundation Model Overview.mp4

03:47

77 - Fine Tuning of Foundation Model Architecture.mp4

03:12

78 - Amazon Bedrock Data Privacy Challenges.mp4

01:36

79 - Fine Tuning of Foundation Models Hands On.mp4

11:00

79 - manufacturingusecase-finetuningdata.zip

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

80 - Amazon CodeWhisperer Introduction.mp4

01:20

81 - Amazon CodeWhisperer Installation.mp4

02:00

82 - Amazon CodeWhisperer Create S3 bucket.mp4

06:51

83 - Amazon CodeWhisperer Create VPC and RDS.mp4

13:52

15 - Python Basics Refresher

84 - Basic Python Programming Refresher Part 1.mp4

08:01

84 - python-refresher.zip

85 - Basic Python Programming Refresher Par 2.mp4

15:11

16 - AWS Lambda Function Refresher

86 - AWS Lambda Overview.mp4

05:25

87 - AWS Lambda Invocation.mp4

03:15

88 - Boto3 Introduction.mp4

09:00

17 - AWS API Gateway Refresher

89 - AWS API Gateway Refresher.mp4

29:57

Description

Build Chatbot, Image Generation, RAG, Text Summarize Apps using Bedrock and Langchain. No prior AI/ML/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
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

Who is this for?

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

What You Need to Know?

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.

More details

Description
Amazon Bedrock and GenAI Course :
Hands - On Use Cases implemented as part of this course
Use Case 1 - Generate Poster Design for Media Industry usingÂ 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) -
Â Â Â Â Â Â Â Â Â Â Â Â Amazon Bedrock (Claude Foundation Model) + Langchain (Ochestrator)+ FAISS (Vector DB)Â + 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 â€“ 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) -
Â Â Â Â Â Â Â Â Â Â Â Â Â Amazon Bedrock (Claude Foundation Model) + Langchain (Ochestrator)+ FAISS (Vector DB)Â + 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 - AWS API Gateway Refresher
Services Used in the Course :
Amazon Bedrock
Llama 2 Foundation Model
Cohere Foundation Model
Stability Diffusion Model
Claude Foundation Model from Anthropic
Bedrock Knowledge Base
Langchain - Chains and Memory Modules
FAISS Vector Store
AWS Code Generation using AWS Code Whisperer
API Gateway
Lambda
Streamlit
S3
Prompt design Techniques (Zero Shot, One Shot.)Â 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
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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Artificial Intelligence

Amazon AWS

Rahul Trisal

Instructor's Courses

Rahul Trisal is a Senior Consultant with more than 15 years’ experience focused on AWS Cloud Strategy, Architecture and Migration. Hands-On experience with Large Scale Data Center Migration to Cloud (200+ applications).Key AWS Services Expertise – Hands on expertise with CloudFormation (YAML), EC2, S3, FSx, EFS, AWS Transfer Family, CloudWatch, Data Sync, Lambda, ELB, Autoscaling, FCI Cluster (SQL DB), Route 53, SSM Automation, Kinesis Data Streams and Firehose, AWS Lambda, Python, Auto Scaling, Auto Healing strategies and many other services.Key Applications Migrated to AWS – Business Apps - Three tier Web Apps, ERP, File Share apps, Infrastructure Apps - DNS application, ControlM, SFTP, Monitoring AppsCreate and post content on AWS Careers, Architecture and Certification on – Udemy Course , my AWS YouTube channel and LinkedIn and conduct in-house trainingsPreviously worked in USA, UK, Africa and Latin America geographies for 10 years (2011- Nov 2019) as Consultant in various roles.Certifications: Cloud: 6X Certified- AWS Certified Solution Architect - Professional- AWS Certified Solution Architect - Associate- AWS SysOps Certified - Associate- IBM Bluemix Cloud Architect- AWS Cloud Practioner- Azure FundamentalAgile: o SAFe Program Consultant (SPC 5.0)o Certified Product Owner (SAFe PO/PM)o Certified Scrum Master (PSM-1)

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