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Generative AI for Research & Development with AWS, Python

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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

Overview

Machine 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

Overview

Login 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

Overview

Expected 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

Overview

Expected 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 FM

Overview

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 Claude

Overview

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 Claude

Overview

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

Overview

Prerequisites - 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 Python

Overview

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 Python

Overview

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 Python

Overview

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
Who this course is for

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

https://www.udemy.com/course/generative-ai-research/

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