Generative AI for Research & Development with AWS, Python from Udemy

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 & Development

Here 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, ML

Course Overview at a Glance
Introduction to AI
Real-World Applications of AI
Machine Learning Overview
Machine Learning Applications
AI and ML: Understanding Their Relationship
Types of Machine Learning: Supervised Learning
Unsupervised ML
Reinforcement ML

Section 2: Foundations of Deep Learning

Introduction to Deep Learning
Deep Leaning, AI and ML
Neural Network

Section 3: Generative AI and Its Applications

Introduction to Generative AI
Real-World Application of Generative AI
Benefits of Generative AI
Relationship Between
Foundation Models in Amazon Bedrock
Various Foundation Models via Amazon Bedrock

Section 6: Exploring Amazon Bedrock Console and Features

Amazon Bedrock Console
Playgrounds Feature in Amazon Bedrock
Builder Tools Features in Amazon Bedrock
Safeguard Feature in Amazon Bedrock
Model Access in Amazon Bedrock

Section 7: Inference Parameters of Foundation Models

Randomness and Diversity
Temperature, Top P, Top K & More
Length Control: Response Length, Stop Sequence, & Length Penalty

Section 8: Gen AI Use Case 1: Text-to-Image Generation with Lambda and Amazon Model

Project Overview
Login to AWS and Access Bedrock Service
Create S3 Bucket and Lambda Function
Configure and Assign Permissions to a Lambda Function
Begin Coding the Lambda Function: Import json and boto3
Send Text Input to Lambda Function
Verify the Boto3 Version
Invoke the Bedrock Model (Titan Image Generator G1)
Inference Parameters
Image Generation Configuration
Required parameters to invoke the model
Print the Model's Response
Arrange Model Response using ChatGPT
Extract the Desired Key-Value from the Model's Response
Extract the Image data using Cloud Watch Logs
Set the S3 Bucket and Object Key
Upload the Image to S3 Bucket
Check the Generated Image in S3 Bucket
Configure Proper Permissions for S3 Bucket
Generate a Presigned URL for Image Access
Verify and Access Image via Presigned URL
Return Statement
Introduction to API Gateway
Create REST API
Pass Query Parameters via API Gateway
Create Mapping Template Body in API Gateway
Final Test through API Gateway

Section 9: Gen AI Use Case 2: Text-to-Image Generation with Lambda and Stable Diffusion

Use Case Overview
Expected Outcome Before Getting Started
Create a Lambda Function and S3 Bucket
Configure and Assign Permissions to a Lambda Function
Begin Coding the Lambda Function: Import json and boto3
Lambda Connection to Bedrock and S3 via Code
Create a Function to Send Input Text to Lambda
Verify Stable Diffusion Model Access by Anthropic
Invoke the Bedrock Model (Stable Diffusion)
Supplying Model Inference Parameters
Print Bedrock Model Response for the Prompt
Convert Model Response from JSON to Python Dictionary
Print the response of the Model
Extract the Desired Key-Value from the Model's Response
Extract the Image data using Cloud Watch Logs
Define the Bucket and Object Key Name
Upload the Image to S3 Bucket
Download and Check Image from S3
Generate a Presigned URL for Image Access
Re-run Lambda to Generate Image URL
Return Statement
Introduction to API Gateway
Create REST API
Provide URL Query String Parameters via API Gateway
Create Template Body in API Gateway Mapping Templates
Final Testing via API Gateway

Section 10: GenAI Use Case 3: Text Summarization Generation Using Cohere Command-Text FM

Use Case Overview
Expected Outcome Before Getting Started
Create and Assign Permissions to a Lambda Function
Lambda Function: Importing json and boto3
Create a Function to Handle Text Input for Summarization
Run the Lambda Function to View the Response
Invoke the Model for Text Summarization - Cohere Command
Supplying Model Inference Parameters
Run the Lambda Function to View the Response
Convert the Response into a Python Dictionary
Extract the Value of the "text" Key
Return the Model Response
Create an API Gateway
Set URL Query Parameters and Create Mapping Template in API Gateway
Final Testing via API Gateway

Section 11: Project - Text2Speech Player

Introduction to the Text2Speech Project
Import Python Libraries: gTTS, os, pygame, time
Function for Text-to-Speech Conversation
Save the speech as an audio file
Initialize pygame mixer for audio playback
Wait for the audio to finish playing
Delete the audio file after playback
Call the function
Run and debug the text-to-speech player code

Section 12: Gen AI Use Case 4: Building a Python-Based Chatbot with AWS Bedrock and Anthropic Claude FM

Overview of the Chatbot Project
Installing and Setting Up VS Code
Create IAM User for Bedrock Access
Authorize VS Code Access to AWS via AWS CLI
Getting Started with Python: Importing JSON and Boto3
Define a Function to Set Up the Bedrock Client
Define a Function to Invoke the Bedrock Model
Passing Parameters to Invoke the Model
Defining Model Inference Parameters
Defining Body Parameters
Call Functions with Arguments in Python
Manually Get User Input and Invoke the Bedrock Model
Display the Model's Response
Response from the Anthropic Model
Troubleshoot and Run Python Code for Chatbot
Run the chatbot in a loop

Section 13: GenAI Use Case 5: Streamlit-Based Python Chatbot with AWS Bedrock and Anthropic Claude

Overview of the Chatbot Project
Introduction to Streamlit for Building a Basic LLM Chat App
Python Code to Invoke the Bedrock Model
Streamlit Python Code for Building a Frontend
Streamlit Python Code - Initialize Chat History
Streamlit Code: Add Button for User Input
Streamlit Code: Clear Chat History
Run the Streamlit Python Chatbot

Section 14: GenAI Use Case 6: LangChain-Driven Streamlit Chatbot Using Python, AWS Bedrock, Anthropic Claude

Overview of LangChain Feature
Chatbot Demo and Architecture Explained
Importing Classes from the LangChain Library
Install VS Code and Start Coding in Python
Initialize FM Parameters with ChatBedrock
Set Model ID and Parameters
Initialize Conversation Memory - ConversationSummaryBufferMemory
Function to Manage Chatbot Conversation - ConversationChain
Streamlit Python Code for Building a Frontend
Troubleshooting
Run Chatbot and Verify LangChain Features

Section 15: GenAI Use Case7: Retrieval Augmented Generation (RAG) - Build a Health Chatbot

Expected Outcome Before Getting Started
Project Overview
Prerequisites - Required Installation and Setup
Importing all necessary Python libraries
Load Internal Data Source with PyPDFLoader
Split the data using RecursiveCharacterTextSplitter
Establish AWS Access in VS Code Using AWS CLI
Create Text Embeddings
Create a function
Create a function to connect with Claude FM
Create a function to search Vector DB for the best match
Streamlit Code for Frontend Development
Verify Python Health Department QA Chatbot

Section 16: Introduction to Python Language

Introduction
An overview of Python
About Shell Scripting
Python vs. Shell Scripting
When to Use Python vs. Shell Scripting

Section 17: How to Begin Practicing Python Coding

Begin Python Coding Practice
Visual Studio Code - Python Coding Practice
PyCharm - IDEs
Codespaces - Online Coding Platform

Section 18: Python Data Types

About Data Types in Python
Lab - String Data Type
Lab - Integer Data Type
Lab - Float Data Type
Lab - len(), Length of a string
Lab - String upper(), lower()
Lab - String replace()
Lab - String split()
Lab - Print specific object in split()
About List in Python
Lab - List Data Type
Lab - Add and Modify in a List Data Type (Mutable)
About Tuples in Python
Lab - Tuples in Python
About Sets in Python
Lab - Sets in Python
Dictionary in Python
Lab - Dictionary in Python
Boolean Data Types
Lab - Boolean in Python

Section 19: Regular Expression (regex) in Python

Overview of Regular Expressions in Python
Lab - Using re. match() to Match Patterns at the Start of a String
Lab - Using re. search() to Find Matches Anywhere in a String
Lab - Using re. findall() to Search for All Matches in a String
Regex Use Cases from a DevOps Perspective
Coding Exercise

Section 20: Mastering Keywords in Python

Overview of Keywords in Python
Common Python keywords
Mastering Control Flow Keywords - if, else, for, and break
Lab: Mastering Control Flow Keywords - continue, def, return, class, import etc.

Section 21: Working with Variables in Python

Overview of Variables with Example
Lab: Working with Float Variables in Python
Lab: Defining Lists as Variables in Python
Lab: Working with Dictionary Variables in Python

Section 22: Return Statement in Python

Return Statement: An Overview with Syntax
Lab: Creating Functions That Return Values
Lab: Functions That Return Multiple Values
Lab: Function for Identifying Even and Odd Values

Section 23: Python Functions: Definition and Usage

Introduction to Functions in Python
Advantages of functions in Python
Lab: Functions with Parameters
Lab: Functions with Return Value
Lab: Designing Functions for Basic Arithmetic Operation-> Comparing Scripts: Using Functions vs. Not Using Functions

Section 24: Utilizing Modules in Function Design

Introduction to Python Modules
An Overview of Built-in Modules
An Overview of User-defined Modules
Lab: Essential Built-in Modules in Python
Lab: OS and Math Modules
Lab: Building Your Own Modules
Last Lecture

What's inside

Learning objectives

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
Show more
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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
Show more
Show less

Syllabus

Introduction

Course Overview at a Glance

Introduction to AI

Real-World Applications of AI

Machine Learning Overview

Machine Learning Applications

AI and ML: Understanding Their Relationship

Types of Machine Learning: Supervised Learning

Unsupervised ML

Reinforcement ML

AI and Machine Learning: Concepts and Applications

The basics and significance of Deep Learning in the context of AI and ML.

Introduction to Deep Learning

Deep Leaning, AI and ML

Neural Network

Deep Learning and Neural Networks

Real-world applications of Generative AI across various industries.

Introduction to Generative AI

Real-World Application of Generative AI

Benefits of Generative AI

Relationship Between AI, ML, DL and Generative AI

Exploring Generative AI

Students will learn about Foundation Models, Large Language Models (LLMs), Text-to-Image generation, and Multimodal AI, and how these technologies are applied in real-world scenarios.

Introduction to Foundation Models

LLM, Text-to-Image Models

Multimodal Models

Different Foundation Models available via Amazon Bedrock and their use cases for text, image, and multimodal generation.

Introduction to Amazon Bedrock

How Amazon Bedrock Works?

Foundation Models in Amazon Bedrock

Various Foundation Models via Amazon Bedrock

Foundation Models, LLMs, and Multimodal Applications

Students will be able to navigate the Amazon Bedrock Console, use Playgrounds, customize models with Builder Tools, implement the Safeguard feature, and access various models.

Amazon Bedrock Console

Playgrounds Feature in Amazon Bedrock

Builder Tools Features in Amazon Bedrock

Safeguard Feature in Amazon Bedrock

Model Access in Amazon Bedrock

Exploring Features and Capabilities

Inference Parameters of Foundation Models

Randomness and Diversity

Temperature, Top P, Top K & More

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

Practice

Students will learn to implement text-to-image generation using AWS Lambda and Amazon AI models, covering integration, Lambda function setup, and efficient use of Amazon's generative AI models.

Project Overview

Create S3 Bucket and Lambda Function

Configure and Assign Permissions to a Lambda Function

Begin Coding the Lambda Function: Import json and boto3

Send Text Input to Lambda Function

Verify the Boto3 Version

Invoke the Bedrock Model (Titan Image Generator G1)

Inference Parameters

Image Generation Configuration

Required parameters to invoke the model

Print the Model's Response

Arrange Model Response using ChatGPT

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

Extract the Image data using Cloud Watch Logs

Set the S3 Bucket and Object Key

Upload the Image to S3 Bucket

Check the Generated Image in S3 Bucket

Configure Proper Permissions for S3 Bucket

Verify and Access Image via Presigned URL

Pass Query Parameters via API Gateway

Create Mapping Template Body in API Gateway

Final Test through API Gateway

For Practice

Students will learn to implement text-to-image generation using AWS Lambda and Amazon AI models, covering integration, Lambda function setup, and efficient use of Antrophic generative AI models.

Use Case Overview

Expected Outcome Before Getting Started

Create a Lambda Function and S3 Bucket

Lambda Connection to Bedrock and S3 via Code

Create a Function to Send Input Text to Lambda

Verify Stable Diffusion Model Access by Anthropic

Invoke the Bedrock Model (Stable Diffusion)

Supplying Model Inference Parameters

Print Bedrock Model Response for the Prompt

Convert Model Response from JSON to Python Dictionary

Print the response of the Model

Define the Bucket and Object Key Name

Download and Check Image from S3

Generate a Presigned URL for Image Access

Re-run Lambda to Generate Image URL

Return Statement

Introduction to API Gateway

Create REST API

Provide URL Query String Parameters via API Gateway

Create Template Body in API Gateway Mapping Templates

Final Testing via API Gateway

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 Generative AI for Research & Development with AWS, Python with these activities:

Review Python Fundamentals

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Strengthen your understanding of Python fundamentals to better grasp the chatbot and scripting aspects of the course.

Browse courses on Python Basics

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Review Python syntax and data structures.
Practice writing basic Python functions.
Complete online Python tutorials or exercises.

Review 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of the machine learning foundations that underpin generative AI models.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters on neural networks and deep learning.
Work through the code examples related to TensorFlow and Keras.
Focus on the sections that discuss model training and evaluation.

Build a Simple Text Summarizer

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Apply your knowledge of generative AI by creating a basic text summarization tool using Cohere or another suitable model.

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Choose a text summarization model from Bedrock.
Write Python code to interact with the model's API.
Create a user interface using Streamlit.
Test and refine your summarization tool.

Four other activities

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Practice Regular Expression Exercises

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Improve your ability to manipulate text data by practicing regular expression exercises in Python.

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Find online resources for regular expression practice.
Work through exercises of varying difficulty levels.
Test your regular expressions using online tools.

Document Your RAG Health Chatbot Project

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Solidify your understanding of Retrieval Augmented Generation (RAG) by creating detailed documentation for your health chatbot project.

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Describe the architecture of your RAG chatbot.
Explain the data sources and embedding techniques used.
Document the code for connecting to Claude FM and the vector database.
Create a user guide for the chatbot.

Optimize Lambda Function Performance

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Enhance your skills in cloud computing by optimizing the performance of your AWS Lambda functions used in the text-to-image generation projects.

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Profile your Lambda function's execution time.
Identify bottlenecks in the code.
Implement optimizations such as caching or concurrency.
Measure the performance improvement.

Read 'Generative AI with Python and TensorFlow 2'

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Expand your knowledge of generative AI techniques and architectures using Python and TensorFlow.

View Generative AI with Python and TensorFlow 2:... on Amazon

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Read the chapters on GANs, VAEs, and other generative models.
Experiment with the code examples provided in the book.
Adapt the techniques to your own projects.

Career center

Learners who complete Generative AI for Research & Development with AWS, Python will develop knowledge and skills that may be useful to these careers:

Generative AI Developer

A Generative AI Developer designs, develops, and implements generative AI models and applications. This individual often works with large language models and multimodal AI to create novel solutions. This course helps build the capacity to create such applications using tools like Bedrock, LangChain and foundation models. The hands-on experience with text-to-image generation, text summarization, and chatbot development directly translates to the skills needed for a generative AI developer. Specifically, the use cases involving AWS Lambda, Stable Diffusion, and Anthropic Claude provide practical experience with industry-relevant technologies. By creating chatbots and AI applications, one can learn how to use generative AI to make real world solutions.

See salaries and explore the career path for Generative AI Developer

Chatbot Developer

A Chatbot Developer specializes in designing, building, and deploying conversational AI applications that interact with users in a natural language. These developers are responsible for creating engaging and effective chatbot experiences. This course helps build the skills for a future Chatbot Developer by covering chatbot development with AWS Bedrock and Anthropic Claude. Several use cases address chatbot construction directly, including Streamlit-based and LangChain-driven chatbots. The course also covers Python, necessary to manage chatbot logic and data processing. The focus on RAG to build a health chatbot is particularly beneficial, as it teaches how to integrate external knowledge sources to improve chatbot accuracy.

See salaries and explore the career path for Chatbot Developer

Machine Learning Engineer

A Machine Learning Engineer focuses on building, deploying, and maintaining machine learning models in production environments. This includes working with various AI models and ensuring their scalability and efficiency. This course helps build a foundation needed to implement generative AI applications and chatbots using Bedrock, LLMs, LangChain, RAG, Python, and Streamlit. The hands-on projects, such as building a Python-based chatbot and implementing Retrieval Augmented Generation for a health chatbot, are especially useful for someone in this role. The course’s focus on using AWS services like Lambda and S3 to manage and deploy AI models makes the course relevant for machine learning engineering.

See salaries and explore the career path for Machine Learning Engineer

Prompt Engineer

Prompt Engineers specialize in crafting effective prompts for large language models to elicit desired outputs. They experiment with different prompts and parameters to optimize model performance and ensure accurate and relevant responses. This course helps the student learn how to effectively use foundation models like Cohere Command and Anthropic Claude. The course covers inference parameters, such as temperature and top P, which are critical for prompt engineering. The hands-on experience with text-to-image generation, text summarization, and chatbot development provides practical insights into how prompts influence model outputs. The course also provides the skills needed to work with LangChain.

See salaries and explore the career path for Prompt Engineer

Cloud Solutions Architect

A Cloud Solutions Architect designs and implements cloud-based solutions using services from providers like AWS. This often involves integrating AI and machine learning services into larger cloud architectures. This course helps build the skills to deploy and manage generative AI applications in the cloud. The course covers AWS services such as Lambda, S3, and API Gateway. Additionally, the hands-on projects involving text-to-image generation and chatbot development provide practical experience with integrating AI models into cloud solutions. The focus on Amazon Bedrock and its features makes the course directly relevant to cloud solution architecture.

See salaries and explore the career path for Cloud Solutions Architect

Research Engineer

A Research Engineer bridges the gap between research and practical application by implementing and testing new algorithms and models developed by research scientists. Research Engineers working on generative AI need to be adept at using machine learning tools. This course may be useful because it shows how to build generative AI applications and chatbots using tools like Bedrock, LLMs, LangChain, and Python. The hands-on projects, such as the Text2Speech player and the RAG-based health chatbot, provide practical experience in implementing AI models. A master's degree is usually required.

See salaries and explore the career path for Research Engineer

Data Scientist

A Data Scientist analyzes large datasets, develops statistical models, and leverages machine learning techniques to extract insights and inform business decisions. This course may be useful by teaching a student how to apply AI and machine learning techniques to real-world projects. The course provides hands-on experience with generative AI models and chatbots, which are increasingly relevant in data science. The course also covers Python coding, regular expressions, and utilizing modules, all of which are essential skills for a data scientist. Finally, the ability to work with generative AI to perform text summarization and build chatbots can enhance the functionality of analytical tools.

See salaries and explore the career path for Data Scientist

Full-Stack Developer

A Full Stack Developer works on both the front-end and back-end components of web applications. As AI becomes more integrated into web applications, a full stack developer needs to know how to integrate AI models. This course helps build the skills needed to integrate generative AI into web applications using Python and Streamlit. The course covers building Streamlit-based chatbots and using LangChain, which can then be integrated into other web applications. The hands-on projects and the use of AWS services can teach a full stack developer how to build interactive web applications.

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AI Consultant

An AI Consultant advises organizations on how to leverage AI technologies to solve business problems and improve efficiency. They assess current processes, identify opportunities for AI adoption, and recommend solutions. This course may be useful by familiarizing one to the real-world applications of generative AI and the capabilities of various foundation models. The course also helps one to explore different use cases, such as text-to-image generation, text summarization, and building chatbots. Moreover, understanding how to use Amazon Bedrock Console and its features is essential for an AI consultant to assess and recommend AI solutions.

See salaries and explore the career path for AI Consultant

AI Research Scientist

An AI Research Scientist investigates and develops new AI algorithms and models, often focusing on generative AI and deep learning techniques. This role typically requires a strong understanding of machine learning principles. This course may be useful by giving one the ability to explore the practical applications of AI through hands-on projects involving text-to-image generation and chatbot development. The course's coverage of foundation models, LLMs, and multimodal AI introduces the student to key concepts in AI research. Moreover, familiarity with Amazon Bedrock Console, Playgrounds, and Builder Tools helps one to explore, test, and customize AI models. A master's degree is usually required.

See salaries and explore the career path for AI Research Scientist

AI Product Manager

An AI Product Manager is responsible for defining the strategy, roadmap, and features for AI-powered products. This role requires a deep understanding of AI technologies and the ability to translate business needs into technical requirements. This course may be useful by familiarizing one to the practical applications of generative AI and the capabilities of various foundation models. The course helps one explore different AI applications, like text-to-image generation and chatbot development. Moreover, understanding how to use Amazon Bedrock Console and its features is essential for an AI product manager to assess and integrate AI technologies into product development.

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Computational Linguist

A Computational Linguist develops computational models of human language, often working on natural language processing tasks. This course may be helpful to a computational linguist to give them an understanding of text summarization and chatbot development with various foundation models. It introduces tools such as AWS Bedrock and Anthropic Claude. The use cases involving text summarization and building chatbots provide practical insights into how language models can be applied. A master's degree is usually required.

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Technical Writer

A Technical Writer creates documentation and guides for software and hardware products. With the rise of AI, there is a growing need for technical writers who can explain AI concepts and tools. This course may be useful by giving one exposure to generative AI models and their applications. The course covers various AI applications, such as text-to-image generation and chatbot development, which can be documented for end-users. Moreover, understanding how to use Amazon Bedrock Console and its features could help technical writers create guides for developers using these tools.

See salaries and explore the career path for Technical Writer

Data Analyst

A Data Analyst examines and interprets complex data sets to provide insights and recommendations. They use statistical tools and techniques to identify trends, patterns, and anomalies in data. This course may be useful if one wants to apply generative AI techniques to enhance data analysis workflows. The course covers Python coding, which is essential for data analysis. Additionally, gaining experience with text summarization and chatbot development using foundation models can help data analysts develop more interactive and user-friendly data analysis tools. Data analysts will likely benefit from the course.

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IT Support Specialist

An IT Support Specialist provides technical assistance to users, troubleshooting hardware and software issues. As AI becomes more integrated into IT systems, support specialists can help users with AI-related problems. This course may be useful by teaching chatbot development and providing a basic understanding of AI concepts. Knowing how to build and use chatbots can help IT support specialists automate responses to common user inquiries. The knowledge could improve the efficiency and effectiveness of IT support services.

See salaries and explore the career path for IT Support Specialist

Generative AI for Research & Development with AWS, Python

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