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Top 13 AWS Projects: From Beginner to Pro

Explore 13 hands-on AWS projects for all levels. Enhance your cloud skills with practical, real-world applications and expert guidance.
Jun 2024  · 12 min read

Amazon Web Services (AWS) is currently the leading cloud service provider, with 31% of the global market share. This means that getting an AWS Certification is crucial to landing many types of tech roles, like data engineer or cloud architect.

However, theoretical knowledge alone won't cut it; hands-on projects are essential to truly understand and leverage AWS capabilities effectively.

This article walks you through a series of AWS projects designed for all skill levels. In the final section, we also explore some DevOps projects focused on microservices, monitoring, and infrastructure as code.

Remember to always close down or terminate the resources you use for a project once you are done. Do not leave any AWS services actively running for more than a few hours to avoid unexpected costs.

Beginner

Intermediate

Advanced

DevOps

1. Hosting a Static Website on Amazon S3

4. Building a Serverless Image Processing System

6. Deploying a Complete Machine Learning Fraud Detection Solution using Amazon Sagemaker

11. Building a Fullstack App Using ECS, Terraform, and CodePipeline

2. Deploying a Simple Python Web Application with AWS Elastic Beanstalk

5. Creating a Chatbot with AWS Lex

7. Building a Recommendation System using MXNET Data on Amazon Sagemaker

12. Building an Automated CloudWatch Alarm Reporting System

3. Deploying SQL Server Databases on Amazon RDS

 

8. Building an Image Classification System with Amazon Sagemaker

13. Building a Containerized Web Application using Amazon ECS and AWS Fargate

 

9. Building a Multimodal RAG Using Foundation Models and AWS

 

10. Building an Agentic LLM Assistant on AWS

AWS Projects for Beginners

If you’re a relative newcomer to AWS, these projects can help you explore some of the basic functionality and get familiar with best practices and more. 

Getting started with AWS: IAM roles and S3

As an AWS beginner, the best way to start is to familiarize yourself with the AWS ecosystem and its services. Our introduction to AWS course will take you through the basics and a few advanced concepts.

The two most commonly used services that any beginner should be very comfortable with are IAM and S3.

AWS Identity and Access Management (IAM)

The IAM service is included with every AWS account. Through IAM, you can add new users and control their access to AWS services and resources.

You'll need to understand how to use IAM and follow the security best practices. These skills are crucial for the AWS projects mentioned in this article.

AWS IAM Overview

Source: AWS

AWS Simple Storage Service (S3)

AWS S3 is probably one of the most popular and most heavily used AWS services. It offers an extremely cost-effective data storage solution and is incredibly simple to set up. Amazon also offers the Elastic File System (EFS) as an alternative storage solution. 

Our AWS storage tutorial explores the different features and benefits of S3 and EFS.

Many of the projects listed in this article will leverage S3 as a storage solution in some way, and you will likely use it extensively in your career. Brush up on what Amazon S3 is, and check out the chapter on AWS storage services from our Introduction to AWS course.

1. Hosting a static website on Amazon S3

To kick off the projects in this article, we start with simple static website hosting. It is recommended that you create your own static website to not only learn how to use a few AWS services but also as a great way to start putting your projects out there, demonstrating your AWS skills in a portfolio that you can send to future employers.

The AWS documentation contains a very easy-to-follow tutorial for configuring a static website using a domain registered with Route 53.

AWS services used in this project:

  • Amazon Route 53 for registering a domain.
  • Amazon S3 for storing your website content.
  • CloudFront to speed up your static website.
  • Lambda@Edge (a feature of CloudFront) for security.

2. Deploying a simple python web application with AWS Elastic Beanstalk

AWS Elastic Beanstalk Overview

Source: AWS

One of the best ways of learning the basic workings of AWS Elastic Beanstalk is to deploy a Python web application with either Flask or Django.

AWS Elastic Beanstalk is a great service for deploying web apps because you get to focus on your application code instead of worrying about managing infrastructure. 

However, you still need to learn a few key concepts to configure your web app correctly, such as environment variables, load balancing, and auto-scaling. This hands-on experience is a good introduction to some key DevOps concepts and practices.

3. Deploying SQL Server databases on Amazon RDS

The Amazon RDS for SQL Server workshop offers a step-by-step walkthrough of Amazon RDS. You'll create a DB instance and learn important concepts relating to backups, security, scaling, optimizing, and monitoring your DB instance.

Intermediate AWS Projects

These projects teach beginners how to leverage cloud services for scalable, efficient solutions while also providing practical experience in handling real-world use cases like image processing and interactive chatbots.

4. Building a serverless image processing system

This project guides you through creating a serverless image processing workflow using AWS Step Functions, AWS Lambda, Amazon DynamoDB, and Amazon SNS.

By leveraging AWS Step Functions to orchestrate multiple services, you'll learn to orchestrate and automate complex workflows, a crucial skill for modern cloud-based solutions.

AWS services used in this project:

  • Amazon Rekognition to identify if a face is present in an uploaded image.
  • AWS CloudFormation for a template that quickly sets up the resources needed for the project
  • AWS step functions to orchestrate the workflow.
  • AWS Lambda to invoke Rekognition.
  • Amazon DynamoDB to store image metadata.
  • Amazon Simple Notification Service (SNS).
  • Amazon EventBridge to trigger the state machine to run when a new image is uploaded.

AWS Serverless Image Processing System Architecture Summary

Source: AWS

5. Creating a chatbot with AWS Lex

Chatbots are a hot topic at the moment, and AWS Lex makes it incredibly simple to set up a chatbot with a web interface that can be integrated into your personal or company website.

AWS Lex Chatbot Demo

Source: AWS

AWS offers a sample Amazon Lex UI project that'll give you some hands-on experience setting up and using a chatbot. Using an Amazon CloudFormation template speeds this up even further with all the resources you need to build and configure the chatbot UI automatically.

AWS services used in this project:

  • Amazon Cognito identity pool to create credentials used to authenticate the Lex API calls from the browser.
  • AWS Lex for the chatbot.
  • Amazon CloudFormation for the template.

Advanced AWS Projects

In this section, we present machine learning and AI-based projects that use a wide range of AWS services.

These projects introduce advanced technologies and skills, enabling you to create impactful, cutting-edge solutions that enhance user experiences and business processes.

Machine learning projects with AWS

Amazon Sagemaker is a fully managed, versatile, and scalable service for building, training, and deploying machine learning models in AWS. It is our tool of choice for advanced AWS projects involving machine learning.

Check out this introduction to Sagemaker tutorial for a full rundown of this service.

6. Deploying a complete machine learning fraud detection solution using Amazon Sagemaker

The first project in this section is an in-depth end-to-end machine learning fraud detection solution created by AWS.

This project will guide you through data preparation, model training, and deployment. You'll gain insight into the entire machine learning process, and in the end, you'll have a fully functional fraud detection system.

AWS Fraud Detection Solution using Amazon Sagemaker: Overview of the machine learning process

Source: AWS

Some basic Python skills are required to navigate the project effectively. However, the code is well-explained, so you should be able to follow along even if you're still learning Python.

7. Building a recommendation system in Amazon Sagemaker

With this project, you will train and deploy a recommendation system using customer ratings data on Amazon Sagemaker.

This project introduces the fundamental concepts used in building recommendation systems. You'll gain practical experience using deep learning concepts and SageMaker for scalable model training and deployment.

Recommendation systems are widely used in e-commerce, content streaming, and other sectors, making this project highly relevant and providing real-world context to the skills learned.

8. Building an image classification pipeline with Amazon Sagemaker and MXNet data

In this project, you'll build an image classification pipeline with Amazon Sagemaker.

This project will introduce you to image classification, a fundamental task in computer vision. However, by using Amazon Sagemaker to do all the heavy lifting, you can focus on understanding the workflow and key concepts without worrying about creating your own custom models.

AI projects with AWS

The second part of advanced projects is focused on AI-based solutions. Generative AI, large language models, and ChatGPT are currently dominating the AI market at the moment, making it a good time to hone your AI skills.

Amazon Bedrock is a fully managed, serverless service that allows you to experiment with and deploy generative AI solutions easily and securely.

Check out this Amazon Bedrock workshop for an introduction to Bedrock and how it works.

9. Building a multimodal RAG using foundation models and AWS

In this first project, you'll build a multimodal RAG using foundation models hosted on Amazon Bedrock and Amazon Sagemaker.

The goal is to extract contextually relevant information from tables, charts, and text in slide decks (like Microsoft PowerPoint, for example). This is where a multimodal RAG is especially useful—when you have data in different formats (like images or text) and need to extract insights from it.

By working with foundation models on Amazon Bedrock and SageMaker, you can get hands-on experience with cutting-edge AI tools. Knowing how to leverage AI to perform complex data extraction and analysis is a highly valuable skill in a variety of industries and applications.

10. Building an agentic LLM assistant on AWS

In this project, you'll build an agentic LLM assistant on AWS using Bedrock, Lambda, Cognito, Aurora, and S3.

This is an in-depth project that offers practical exposure to an extensive three-tier architecture. It will also realistically demonstrate how an AI-based solution is developed and deployed on a cloud-based platform.

This project also covers critical aspects of user authentication with Cognito and data management with Aurora and S3, providing a well-rounded understanding of secure and efficient application development.

AWS Agentic LLM Assistant Architecture Summary

Source: AWS

AWS DevOps Projects

DevOps involves five main areas or best practices:

  • Continuous integration and continuous delivery
  • Microservices
  • Infrastructure as code
  • Monitoring and logging
  • Communication and collaboration

AWS offers services that address all of these areas, making it a good choice to learn and master if you want to build a career in DevOps. In this section, we present projects that address three of the above areas using AWS services.

11. Building a fullstack app using ECS, Terraform, and CodePipeline

This project demonstrates how to use AWS services to create a robust architecture for deploying a full-stack application.

It highlights the benefits of using managed container services and Infrastructure as Code (IaC) for efficient and reliable application deployment.

You'll get hands-on experience with key AWS services like ECS for container management, Terraform for Infrastructure as Code, and CodePipeline for CI/CD, equipping you with essential cloud skills.

By integrating continuous integration and continuous deployment (CI/CD) pipelines, this project also introduces you to modern DevOps practices. You'll learn how to deploy applications more reliably and efficiently. These skills are crucial for maintaining a smooth and automated workflow in production environments.

AWS Full-Stack Application with ECS, Terraform, and CodePipeline Architecture Summary

Source: AWS

12. Building an automated CloudWatchalarm reporting system

This project guides you through creating an automated CloudWatch alarm reporting system that generates daily reports of triggered Amazon CloudWatch alarms in a specified AWS region. The report, created by an AWS Lambda function triggered by Amazon EventBridge, is saved as a CSV file in an S3 bucket and emailed via Amazon SES.

This project introduces you to automating tasks using AWS services like Lambda, EventBridge, and SES. It showcases how to streamline monitoring and reporting processes, which is an important aspect of DevOps.

It also demonstrates how to integrate multiple AWS services (Lambda, S3, SES) to create a cohesive solution, teaching you the importance of service orchestration in cloud environments.

AWS Automated CloudWatch Alarm Reporting System Architecture SummarySource: AWS

13. Building a containerized web application using Amazon ECS and AWS Fargate

This project involves building and deploying a containerized web application using Amazon ECS and Fargate. The application displays random cat or dog pictures based on user selection.

You'll get hands-on experience with containerization using Docker and orchestration with Amazon ECS and Fargate.

AWS Fargate is an essential DevOps tool for managing and running containers without the need to provision, configure, or scale clusters of virtual machines.

The project also covers load testing, teaching you how to evaluate and ensure the scalability and reliability of your web applications under different traffic conditions.

AWS Containerized Web App Using Amazon ECS and AWS Fargate Demo Process

Source: AWS

Conclusion

This article presents a series of AWS projects designed to enhance skills at all levels. It emphasizes the importance of hands-on projects for gaining practical experience and proficiency in AWS services, which is essential for various tech roles.

Whether you're aiming to pursue a career in cloud technology or simply looking to enhance your AWS skills, the projects outlined in this article offer valuable learning opportunities.

To build a strong career in AWS, start by signing up for our Introduction to AWS course and then work your way up to getting certified. For a peek into the career and salary options for an AWS cloud practitioner role, check out our detailed guide next! 

AWS Project FAQs

What are some beginner-friendly AWS projects to start with?

Beginner-friendly AWS projects include hosting a static website on Amazon S3, deploying a simple web application with AWS Elastic Beanstalk, and setting up a basic serverless API using AWS Lambda and API Gateway. These projects provide foundational knowledge of AWS services and their practical applications.

What is the cost of running typical AWS projects?

The cost of running typical AWS projects varies based on the services used, the scale of the project, and usage patterns. AWS offers a Free Tier with limited access to many services, allowing beginners to experiment with minimal costs, but it's essential to monitor usage to avoid unexpected charges.

How do I deploy a web application on AWS?

To deploy a web application on AWS, you can use services like AWS Elastic Beanstalk for simple, scalable deployments or Amazon ECS for containerized applications. The process typically involves preparing your application, creating an environment in the chosen service, and deploying your code.

How do I automate deployments and workflows on AWS?

You can automate deployments and workflows on AWS using services like AWS CodePipeline for continuous integration and continuous delivery (CI/CD), AWS CloudFormation for infrastructure as code, and AWS Step Functions for orchestrating complex workflows. These tools help streamline processes and reduce manual intervention.

What tools and services do I need to know for an AWS DevOps project?

For an AWS DevOps project, you should be familiar with AWS CodePipeline for CI/CD, AWS CodeBuild for building and testing code, AWS CodeDeploy for automated deployments, and AWS CloudFormation for infrastructure management. Additionally, knowledge of AWS CloudWatch for monitoring and logging is essential.


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