Generative Ai For Research & Development With Aws, Python

Learn to build AI apps and chatbots using Bedrock, LLMs, LangChain, RAG, Python, Streamlit, and Generative AI for RD.

What you'll learn

Introduction to AI, ML, and Neural Networks

Students will gain insight into real-world applications of AI.

Students will gain an understanding of the foundations of Deep Learning.

Learn how Generative AI works and deep dive into Foundation Models.

Students will learn about Foundation Models, LLMs, Text-to-Image generation, and Multimodal AI, and their real-world applications.

Students will learn to use Amazon Bedrock Console, Playgrounds, Builder Tools, Safeguard, and models.

Use Case 1: Text-to-image generation with AWS Lambda and Amazon AI models, including setup.

Use Case 2: Text-to-image generation with AWS Lambda and Stable Diffusion AI models.

Use Case 3: Text summarization using Cohere Command and Text Foundation Models.

Use Case 4: Python-Based Chatbot with AWS Bedrock and Anthropic Claude FM

Use Case 5: Streamlit-Based Python Chatbot with AWS Bedrock and Anthropic Claude

Use Case 6: LangChain-Driven Streamlit Chatbot Using Python, AWS Bedrock, Anthropic Claude

Use Case 7: Retrieval Augmented Generation (RAG) - Build a Health Chatbot

Project - Text2Speech Player, students will develop a Text-to-Speech (TTS) player using Python libraries such as gTTS, os, and pygame.

Python coding practice

Regular Expression (regex) in Python

Mastering Keywords in Python

How to declare and assign values to variables.

Python Functions: Definition and Usage

How to Begin Practicing Python Coding

Return Statement in Python

Requirements

Basic Computer Skills: Familiarity with using a computer and navigating the internet.

You need to have an AWS account.

A basic understanding of Python is required.

Description

In this course, you will learn how to build generative AI applications and chatbots using Bedrock, LLMs, LangChain, RAG, Python, Streamlit, and various foundation models, with a focus on their application in research and development for real-world projects.Generative AI for Research & DevelopmentHere are the key use cases and projects featured in the course:Text-to-Image Generation: Learn how to use AWS Lambda and Amazon AI models to generate images from text, with a full setup guide.Text-to-Image Generation with Stable Diffusion: Explore how to integrate Stable Diffusion models for generating images based on text input.Text Summarization: Understand how to use Cohere Command and Text Foundation Models for efficient text summarization.Python-Based Chatbot: Build a chatbot using AWS Bedrock and Anthropic Claude FM.Streamlit-Based Python Chatbot: Create a dynamic, Streamlit-powered Python chatbot with AWS Bedrock and Anthropic Claude.LangChain-Driven Chatbot: Build a LangChain-powered Streamlit chatbot using Python, AWS Bedrock, and Anthropic Claude.RAG for Health Chatbot: Implement Retrieval Augmented Generation (RAG) to develop a health-related chatbot.Project: Text2Speech Player - A hands-on project where students will develop a Text-to-Speech (TTS) player using Python libraries like gTTS, os, and pygame.Section 1: Introduction to AI, MLCourse Overview at a GlanceIntroduction to AIReal-World Applications of AIMachine Learning OverviewMachine Learning ApplicationsAI and ML: Understanding Their RelationshipTypes of Machine Learning: Supervised LearningUnsupervised MLReinforcement MLSection 2: Foundations of Deep LearningIntroduction to Deep LearningDeep Leaning, AI and MLNeural NetworkSection 3: Generative AI and Its ApplicationsIntroduction to Generative AIReal-World Application of Generative AIBenefits of Generative AIRelationship Between AI, ML, DL and Generative AISection 4: Foundation Models, LLMs, Text-to-Image, and Multimodal AIIntroduction to Foundation ModelsLLM, Text-to-Image ModelsMultimodal ModelsSection 5: Amazon Bedrock and Foundation Models: An In-Depth ExplorationIntroduction to Amazon BedrockHow Amazon Bedrock Works?Foundation Models in Amazon BedrockVarious Foundation Models via Amazon BedrockSection 6: Exploring Amazon Bedrock Console and FeaturesAmazon Bedrock ConsolePlaygrounds Feature in Amazon BedrockBuilder Tools Features in Amazon BedrockSafeguard Feature in Amazon BedrockModel Access in Amazon BedrockSection 7: Inference Parameters of Foundation ModelsRandomness and DiversityTemperature, Top P, Top K & MoreLength Control: Response Length, Stop Sequence, & Length PenaltySection 8: Gen AI Use Case 1: Text-to-Image Generation with Lambda and Amazon ModelProject OverviewLogin to AWS and Access Bedrock ServiceCreate S3 Bucket and Lambda FunctionConfigure and Assign Permissions to a Lambda FunctionBegin Coding the Lambda Function: Import json and boto3Send Text Input to Lambda FunctionVerify the Boto3 VersionInvoke the Bedrock Model (Titan Image Generator G1)Inference ParametersImage Generation ConfigurationRequired parameters to invoke the modelPrint the Model's ResponseArrange Model Response using ChatGPTExtract the Desired Key-Value from the Model's ResponseExtract the Image data using Cloud Watch LogsSet the S3 Bucket and Object KeyUpload the Image to S3 BucketCheck the Generated Image in S3 BucketConfigure Proper Permissions for S3 BucketGenerate a Presigned URL for Image AccessVerify and Access Image via Presigned URLReturn StatementIntroduction to API GatewayCreate REST APIPass Query Parameters via API GatewayCreate Mapping Template Body in API GatewayFinal Test through API GatewaySection 9: Gen AI Use Case 2: Text-to-Image Generation with Lambda and Stable DiffusionUse Case OverviewExpected Outcome Before Getting StartedCreate a Lambda Function and S3 BucketConfigure and Assign Permissions to a Lambda FunctionBegin Coding the Lambda Function: Import json and boto3Lambda Connection to Bedrock and S3 via CodeCreate a Function to Send Input Text to LambdaVerify Stable Diffusion Model Access by AnthropicInvoke the Bedrock Model (Stable Diffusion)Supplying Model Inference ParametersPrint Bedrock Model Response for the PromptConvert Model Response from JSON to Python DictionaryPrint the response of the ModelExtract the Desired Key-Value from the Model's ResponseExtract the Image data using Cloud Watch LogsDefine the Bucket and Object Key NameUpload the Image to S3 BucketDownload and Check Image from S3Generate a Presigned URL for Image AccessRe-run Lambda to Generate Image URLReturn StatementIntroduction to API GatewayCreate REST APIProvide URL Query String Parameters via API GatewayCreate Template Body in API Gateway Mapping TemplatesFinal Testing via API GatewaySection 10: GenAI Use Case 3: Text Summarization Generation Using Cohere Command-Text FMUse Case OverviewExpected Outcome Before Getting StartedCreate and Assign Permissions to a Lambda FunctionLambda Function: Importing json and boto3Create a Function to Handle Text Input for SummarizationRun the Lambda Function to View the ResponseInvoke the Model for Text Summarization - Cohere CommandSupplying Model Inference ParametersRun the Lambda Function to View the ResponseConvert the Response into a Python DictionaryExtract the Value of the "text" KeyReturn the Model ResponseCreate an API GatewaySet URL Query Parameters and Create Mapping Template in API GatewayFinal Testing via API GatewaySection 11: Project - Text2Speech PlayerIntroduction to the Text2Speech ProjectImport Python Libraries: gTTS, os, pygame, timeFunction for Text-to-Speech ConversationSave the speech as an audio fileInitialize pygame mixer for audio playbackWait for the audio to finish playingDelete the audio file after playbackCall the functionRun and debug the text-to-speech player codeSection 12: Gen AI Use Case 4: Building a Python-Based Chatbot with AWS Bedrock and Anthropic Claude FMOverview of the Chatbot ProjectInstalling and Setting Up VS CodeCreate IAM User for Bedrock AccessAuthorize VS Code Access to AWS via AWS CLIGetting Started with Python: Importing JSON and Boto3Define a Function to Set Up the Bedrock ClientDefine a Function to Invoke the Bedrock ModelPassing Parameters to Invoke the ModelDefining Model Inference ParametersDefining Body ParametersCall Functions with Arguments in PythonManually Get User Input and Invoke the Bedrock ModelDisplay the Model's ResponseResponse from the Anthropic ModelTroubleshoot and Run Python Code for ChatbotRun the chatbot in a loopSection 13: GenAI Use Case 5: Streamlit-Based Python Chatbot with AWS Bedrock and Anthropic ClaudeOverview of the Chatbot ProjectIntroduction to Streamlit for Building a Basic LLM Chat AppPython Code to Invoke the Bedrock ModelStreamlit Python Code for Building a FrontendStreamlit Python Code - Initialize Chat HistoryStreamlit Code: Add Button for User InputStreamlit Code: Clear Chat HistoryRun the Streamlit Python ChatbotSection 14: GenAI Use Case 6: LangChain-Driven Streamlit Chatbot Using Python, AWS Bedrock, Anthropic ClaudeOverview of LangChain FeatureChatbot Demo and Architecture ExplainedImporting Classes from the LangChain LibraryInstall VS Code and Start Coding in PythonInitialize FM Parameters with ChatBedrockSet Model ID and ParametersInitialize Conversation Memory - ConversationSummaryBufferMemoryFunction to Manage Chatbot Conversation - ConversationChainStreamlit Python Code for Building a FrontendTroubleshootingRun Chatbot and Verify LangChain FeaturesSection 15: GenAI Use Case7: Retrieval Augmented Generation (RAG) - Build a Health ChatbotExpected Outcome Before Getting StartedProject OverviewPrerequisites - Required Installation and SetupImporting all necessary Python librariesLoad Internal Data Source with PyPDFLoaderSplit the data using RecursiveCharacterTextSplitterEstablish AWS Access in VS Code Using AWS CLICreate Text EmbeddingsCreate a functionCreate a function to connect with Claude FMCreate a function to search Vector DB for the best matchStreamlit Code for Frontend DevelopmentVerify Python Health Department QA ChatbotSection 16: Introduction to Python LanguageIntroductionAn overview of PythonAbout Shell ScriptingPython vs. Shell ScriptingWhen to Use Python vs. Shell ScriptingSection 17: How to Begin Practicing Python CodingBegin Python Coding PracticeVisual Studio Code - Python Coding PracticePyCharm - IDEsCodespaces - Online Coding PlatformSection 18:  Python Data TypesAbout Data Types in PythonLab - String Data TypeLab - Integer Data TypeLab - Float Data TypeLab - len(), Length of a stringLab - String upper(), lower()Lab - String replace()Lab - String split()Lab - Print specific object in split()About List in PythonLab - List Data TypeLab - Add and Modify in a List Data Type (Mutable)About Tuples in PythonLab - Tuples in PythonAbout Sets in PythonLab - Sets in PythonDictionary in PythonLab - Dictionary in PythonBoolean Data TypesLab - Boolean in PythonSection 19: Regular Expression (regex) in PythonOverview of Regular Expressions in PythonLab - Using re. match() to Match Patterns at the Start of a StringLab - Using re. search() to Find Matches Anywhere in a StringLab - Using re. findall() to Search for All Matches in a StringRegex Use Cases from a DevOps PerspectiveCoding ExerciseSection 20: Mastering Keywords in PythonOverview of Keywords in PythonCommon Python keywordsMastering Control Flow Keywords - if, else, for, and breakLab: Mastering Control Flow Keywords - continue, def, return, class, import etc.Section 21: Working with Variables in PythonOverview of Variables with ExampleLab: Working with Float Variables in PythonLab: Defining Lists as Variables in PythonLab: Working with Dictionary Variables in PythonSection 22: Return Statement in PythonReturn Statement: An Overview with SyntaxLab: Creating Functions That Return ValuesLab: Functions That Return Multiple ValuesLab: Function for Identifying Even and Odd ValuesSection 23: Python Functions: Definition and UsageIntroduction to Functions in PythonAdvantages of functions in PythonLab: Functions with ParametersLab: Functions with Return ValueLab: Designing Functions for Basic Arithmetic Operation-> Comparing Scripts: Using Functions vs. Not Using FunctionsSection 24: Utilizing Modules in Function DesignIntroduction to Python ModulesAn Overview of Built-in ModulesAn Overview of User-defined ModulesLab: Essential Built-in Modules in PythonLab: OS and Math ModulesLab: Building Your Own ModulesLast Lecture

Overview

Section 1: Introduction

Lecture 1 Course Overview at a Glance

Lecture 2 Introduction to AI

Lecture 3 Real-World Applications of AI

Lecture 4 Machine Learning Overview

Lecture 5 Machine Learning Applications

Lecture 6 AI and ML: Understanding Their Relationship

Lecture 7 Types of Machine Learning: Supervised Learning

Lecture 8 Unsupervised ML

Lecture 9 Reinforcement ML

Section 2: Foundations of Deep Learning

Lecture 10 Introduction to Deep Learning

Lecture 11 Deep Leaning, AI and ML

Lecture 12 Neural Network

Section 3: Introduction to Generative AI and Its Applications

Lecture 13 Introduction to Generative AI

Lecture 14 Real-World Application of Generative AI

Lecture 15 Benefits of Generative AI

Lecture 16 Relationship Between AI, ML, DL and Generative AI

Section 4: Foundation Models, LLMs, Text-to-Image, and Multimodal AI

Lecture 17 Introduction to Foundation Models

Lecture 18 LLM, Text-to-Image Models

Lecture 19 Multimodal Models

Section 5: Amazon Bedrock and Foundation Models: An In-Depth Exploration

Lecture 20 Introduction to Amazon Bedrock

Lecture 21 How Amazon Bedrock Works?

Lecture 22 Foundation Models in Amazon Bedrock

Lecture 23 Various Foundation Models via Amazon Bedrock

Section 6: Exploring Amazon Bedrock Console and Features

Lecture 24 Amazon Bedrock Console

Lecture 25 Playgrounds Feature in Amazon Bedrock

Lecture 26 Builder Tools Features in Amazon Bedrock

Lecture 27 Safeguard Feature in Amazon Bedrock

Lecture 28 Model Access in Amazon Bedrock

Section 7: Inference Parameters of Foundation Models

Lecture 29 Randomness and Diversity

Lecture 30 Temperature, Top P, Top K & More

Lecture 31 Length Control: Response Length, Stop Sequence, & Length Penalty

Section 8: Generative AI Use Case: Text-to-Image Generation with Lambda and Amazon Model

Lecture 32 Project Overview

Lecture 33 Login to AWS and Access Bedrock Service

Lecture 34 Create S3 Bucket and Lambda Function

Lecture 35 Configure and Assign Permissions to a Lambda Function

Lecture 36 Begin Coding the Lambda Function: Import json and boto3

Lecture 37 Send Text Input to Lambda Function

Lecture 38 Verify the Boto3 Version

Lecture 39 Invoke the Bedrock Model (Titan Image Generator G1)

Lecture 40 Inference Parameters

Lecture 41 Image Generation Configuration

Lecture 42 Required parameters to invoke the model

Lecture 43 Print the Model's Response

Lecture 44 Arrange Model Response using ChatGPT

Lecture 45 Extract the Desired Key-Value from the Model's Response

Lecture 46 Extract the Image data using Cloud Watch Logs

Lecture 47 Set the S3 Bucket and Object Key

Lecture 48 Upload the Image to S3 Bucket

Lecture 49 Check the Generated Image in S3 Bucket

Lecture 50 Configure Proper Permissions for S3 Bucket

Lecture 51 Generate a Presigned URL for Image Access

Lecture 52 Verify and Access Image via Presigned URL

Lecture 53 Return Statement

Lecture 54 Introduction to API Gateway

Lecture 55 Create REST API

Lecture 56 Pass Query Parameters via API Gateway

Lecture 57 Create Mapping Template Body in API Gateway

Lecture 58 Final Test through API Gateway

Section 9: Generative AI Use Case:Text-to-Image Generation with Lambda and Stable Diffusion

Lecture 59 Use Case Overview

Lecture 60 Expected Outcome Before Getting Started

Lecture 61 Create a Lambda Function and S3 Bucket

Lecture 62 Configure and Assign Permissions to a Lambda Function

Lecture 63 Begin Coding the Lambda Function: Import json and boto3

Lecture 64 Lambda Connection to Bedrock and S3 via Code

Lecture 65 Create a Function to Send Input Text to Lambda

Lecture 66 Verify Stable Diffusion Model Access by Anthropic

Lecture 67 Invoke the Bedrock Model (Stable Diffusion)

Lecture 68 Supplying Model Inference Parameters

Lecture 69 Print Bedrock Model Response for the Prompt

Lecture 70 Convert Model Response from JSON to Python Dictionary

Lecture 71 Print the response of the Model

Lecture 72 Extract the Desired Key-Value from the Model's Response

Lecture 73 Extract the Image data using Cloud Watch Logs

Lecture 74 Define the Bucket and Object Key Name

Lecture 75 Upload the Image to S3 Bucket

Lecture 76 Download and Check Image from S3

Lecture 77 Generate a Presigned URL for Image Access

Lecture 78 Re-run Lambda to Generate Image URL

Lecture 79 Return Statement

Lecture 80 Introduction to API Gateway

Lecture 81 Create REST API

Lecture 82 Provide URL Query String Parameters via API Gateway

Lecture 83 Create Template Body in API Gateway Mapping Templates

Lecture 84 Final Testing via API Gateway

Section 10: Use Case: Text Summarization Generation Using Cohere Command-Text FM

Lecture 85 Use Case Overview

Lecture 86 Expected Outcome Before Getting Started

Lecture 87 Create and Assign Permissions to a Lambda Function

Lecture 88 Lambda Function: Importing json and boto3

Lecture 89 Create a Function to Handle Text Input for Summarization

Lecture 90 Run the Lambda Function to View the Response

Lecture 91 Invoke the Model for Text Summarization - Cohere Command

Lecture 92 Supplying Model Inference Parameters

Lecture 93 Run the Lambda Function to View the Response

Lecture 94 Convert the Response into a Python Dictionary

Lecture 95 Extract the Value of the "text" Key

Lecture 96 Return the Model Response

Lecture 97 Create an API Gateway

Lecture 98 Set URL Query Parameters and Create Mapping Template in API Gateway

Lecture 99 Final Testing via API Gateway

Section 11: Project - Text2Speech Player

Lecture 100 Introduction to the Text2Speech Project

Lecture 101 Import Python Libraries: gTTS, os, pygame, time

Lecture 102 Function for Text-to-Speech Conversation

Lecture 103 Save the speech as an audio file

Lecture 104 Initialize pygame mixer for audio playback

Lecture 105 Wait for the audio to finish playing

Lecture 106 Delete the audio file after playback

Lecture 107 Call the function

Lecture 108 Run and debug the text-to-speech player code

Section 12: Building a Python-Based Chatbot with AWS Bedrock and Anthropic Claude FM

Lecture 109 Overview of the Chatbot Project

Lecture 110 Installing and Setting Up VS Code

Lecture 111 Create IAM User for Bedrock Access

Lecture 112 Authorize VS Code Access to AWS via AWS CLI

Lecture 113 Getting Started with Python: Importing JSON and Boto3

Lecture 114 Define a Function to Set Up the Bedrock Client

Lecture 115 Define a Function to Invoke the Bedrock Model

Lecture 116 Passing Parameters to Invoke the Model

Lecture 117 Defining Model Inference Parameters

Lecture 118 Defining Body Parameters

Lecture 119 Call Functions with Arguments in Python

Lecture 120 Manually Get User Input and Invoke the Bedrock Model

Lecture 121 Display the Model's Response

Lecture 122 Response from the Anthropic Model

Lecture 123 Troubleshoot and Run Python Code for Chatbot

Lecture 124 Run the chatbot in a loop

Section 13: Streamlit-Based Python Chatbot with AWS Bedrock and Anthropic Claude

Lecture 125 Overview of the Chatbot Project

Lecture 126 Introduction to Streamlit for Building a Basic LLM Chat App

Lecture 127 Python Code to Invoke the Bedrock Model

Lecture 128 Streamlit Python Code for Building a Frontend

Lecture 129 Streamlit Python Code - Initialize Chat History

Lecture 130 Streamlit Code: Add Button for User Input

Lecture 131 Streamlit Code: Clear Chat History

Lecture 132 Run the Streamlit Python Chatbot

Section 14: LangChain-Driven Streamlit Chatbot Using Python, AWS Bedrock, Anthropic Claude

Lecture 133 Overview of LangChain Feature

Lecture 134 Chatbot Demo and Architecture Explained

Lecture 135 Importing Classes from the LangChain Library

Lecture 136 Install VS Code and Start Coding in Python

Lecture 137 Initialize FM Parameters with ChatBedrock

Lecture 138 Set Model ID and Parameters

Lecture 139 Initialize Conversation Memory - ConversationSummaryBufferMemory

Lecture 140 Function to Manage Chatbot Conversation - ConversationChain

Lecture 141 Streamlit Python Code for Building a Frontend

Lecture 142 Troubleshooting

Lecture 143 Run Chatbot and Verify LangChain Features

Section 15: Use Case: Retrieval Augmented Generation (RAG) - Build a Health Chatbot

Lecture 144 Expected Outcome Before Getting Started

Lecture 145 Project Overview

Lecture 146 Prerequisites - Required Installation and Setup

Lecture 147 Importing all necessary Python libraries

Lecture 148 Load Internal Data Source with PyPDFLoader

Lecture 149 Split the data using RecursiveCharacterTextSplitter

Lecture 150 Establish AWS Access in VS Code Using AWS CLI

Lecture 151 Create Text Embeddings

Lecture 152 Create a function

Lecture 153 Create a function to connect with Claude FM

Lecture 154 Create a function to search Vector DB for the best match

Lecture 155 Streamlit Code for Frontend Development

Lecture 156 Verify Python Health Department QA Chatbot

Section 16: Introduction to the Python Language

Lecture 157 Getting Started with Python Programming

Lecture 158 About Shell Scripting

Lecture 159 Python vs. Shell Scripting

Lecture 160 When to Use Python vs. Shell Scripting

Section 17: How to Begin Practicing Python Coding

Lecture 161 Begin Python Coding Practice

Lecture 162 Visual Studio Code - Python Coding Practice

Lecture 163 PyCharm - IDEs

Lecture 164 Codespaces - Online Coding Platform

Section 18: Python Data Types

Lecture 165 About Data Types in Python

Lecture 166 Lab - String Data Type

Lecture 167 Lab - Integer Data Type

Lecture 168 Lab - Float Data Type

Lecture 169 Lab - len(), Length of a string

Lecture 170 Lab - String upper(), lower()

Lecture 171 Lab - String replace()

Lecture 172 Lab - String split()

Lecture 173 Lab - Print specific object in split()

Lecture 174 About List in Python

Lecture 175 Lab - List Data Type

Lecture 176 Lab - Add and Modify in a List Data Type (Mutable)

Lecture 177 About Tuples in Python

Lecture 178 Lab - Tuples in Python

Lecture 179 About Sets in Python

Lecture 180 Lab - Sets in Python

Lecture 181 Dictionary in Python

Lecture 182 Lab - Dictionary in Python

Section 19: Regular Expression (regex) in Python

Lecture 183 Overview of Regular Expressions in Python

Lecture 184 Lab - Using re.match() to Match Patterns at the Start of a String

Lecture 185 Lab - Using re.search() to Find Matches Anywhere in a String

Lecture 186 Lab - Using re.findall() to Search for All Matches in a String

Lecture 187 Regex Use Cases from a DevOps Perspective

Section 20: Mastering Keywords in Python

Lecture 188 Overview of Keywords in Python

Lecture 189 Common Python keywords

Lecture 190 Mastering Control Flow Keywords - if, else, for, and break

Lecture 191 Mastering Control Flow Keywords - continue, def, return, class, import etc.

Section 21: Working with Variables in Python

Lecture 192 Overview of Variables with Example

Lecture 193 Lab: Working with Float Variables in Python

Lecture 194 Lab: Defining Lists as Variables in Python

Lecture 195 Lab: Working with Dictionary Variables in Python

Section 22: Python Functions and Return Statement

Lecture 196 Introduction to Functions in Python

Lecture 197 Advantages of functions in Python

Lecture 198 Lab: Functions with Parameters

Lecture 199 Return Statement: An Overview with Syntax

Lecture 200 Lab: Creating Functions That Return Values

Lecture 201 Lab: Functions That Return Multiple Values

Lecture 202 Lab: Function for Identifying Even and Odd Values

Lecture 203 Lab: Designing Functions for Basic Arithmetic Operations

Lecture 204 Comparing Scripts: Using Functions vs. Not Using Functions

Section 23: Modules in Function Design

Lecture 205 Introduction to Python Modules

Lecture 206 An Overview of Built-in Modules

Lecture 207 An Overview of User-defined Modules

Lecture 208 Lab: Essential Built-in Modules in Python

Lecture 209 Lab: OS and Math Modules

Lecture 210 Lab: Building Your Own Modules

Lecture 211 Last Lecture

This course is designed to help you change careers and move into well-paying jobs in Generative AI and Amazon Bedrock.