Hands-on Projects to Start Your Journey with Amazon Web Services

Simple & easy to implement AWS projects for your portfolio

In this article, I will give a little introduction to Amazon Web Services and share 5 simple and hands-on projects that you can work on to get started. These projects will give you some insight on what is achievable using AWS platform. You will find the titles of the projects very familiar if you have experience with Python programming. Most of them are machine learning and artificial intelligence projects. When it comes to cloud computing there are specific tools for specific tasks, understanding the toolkit with make things much easier and faster. That’s why, I’ve added the tool/ service names under each project. 

Let’s get started. So what is AWS?

Amazon Web Service Program is an initiative launched to assist aspirant developers. Amazon stands as one of the first companies which came up with a cloud computing business model, and now they stand among the highest few.

Amazon Web Services was launched back in 2006 to provide cloud computing platforms. It provides you with services like compute power, content delivery, database storage, and other such functionalities. These cloud computing solutions are secure, highly scalable, and cost-effective. It manages and maintains infrastructure and hardware for you. One can avail himself of these resources for free or on a pay-per-use basis.

Why is it Important?

AWS has been a beneficial addition that has posed a 180-degree change in the industry.

  • It helps companies by taking off their workload.
  • It helps in managing resources in a cost-optimized way.
  • It has an incredibly diverse collection of tools to make things ten times easier for you.
  • The unlimited server capacity allows you to handle multiple items and grow as much as possible.
  • It provides reliable encryption and makes your data safer and more securer.

We have some idea of the platform. Now, let’s get to the projects.


  • Create a Website
  • Serverless Web Application
  • Building a Content Recommender
  • Face Recognizer using Rekognition
  • Create a Chatbot using Lex

1. Create a Website

Using the AWS cloud platform, you can create websites effortlessly. For example, creating a WordPress website relating to any niche you want. If you decided to work on this project; Amazon Lightsail is the tool you need to know, which I prefer when creating websites or web applications on AWS. It also provides an option of SSD storage, and the interface is relatively easy to navigate.

2. Serverless Web Application

Serverless web applications are one of the advanced programs. You can easily build one using the AWS. You can utilize AWS Amplify, Cognition, Dynamodb, Gateway, and Lambda features/ tools to build a perfect serverless application. If you are planning on working on this project, it will be a plus to have some experience with HTML, CSS, and JavaScript. 

Here is a nice and informative article by Hemant JainBuilding a Serverless Web Application — Part 1

3. Building a Content Recommender

Building and running a content recommendation system is another cool project that I would like to share. In machine learning, it is one of the first projects that everyone starts with to have some idea. And the industry is using content recommender a lot, especially in marketing business. Companies like YouTube, Netflix and Amazon uses it to recommend similar videos/ movies/ products, just some examples that I can give from top of my head. 

With AWS, you can build a content recommender that will work by putting the nearest neighbor algorithm into use. For this project, SageMaker is the tool that we would like to use, it simplifies our task considerably. SageMaker can be used for different machine learning projects too, you are not limited to content recommender.

Building a Movie Recommender using Python
Simple and hands-on machine learning project using scikit-learntowardsdatascience.com

4. Face Recognizer using Rekognition

Face recognition models are being used in latest mobile devices for advanced protection. If you want to build a similar custom model, you should use the AWS Rekognition tool. Face recognizer is another great field to get started with machine learning and computer vision. AWS Rekognition is great for computer vision projects. You can use it to train videos too, not just photos. 

Here is an application idea for interested people. Create an application that can be use to clock in/ clock out using the face of an employee. This is something that I am working on right now with a friend. Hoping to share the development process in a future article. Stay tuned! 

Building a Face Recognizer in Python
Step-by-step guide to face recognition in real-time using OpenCv librarytowardsdatascience.com

5. Create a Chatbot using Lex

Chatbots have been in use, assisting companies by reducing the overall cost and strengthening communication simultaneously. I am sure we all had a conversation with a chatbot recently. They are everywhere right now.

Amazon Lex provides exceptional deep learning functionalities of automatic speech recognition (ASR) for transforming speech to text and natural language understanding (NLU) to recognize the intention of the text. These are really advanced features.

Amazon Lex can build a chatbot for you with just a single click. You can train the program by adding just a few phrases. It will also make your chatbot sound so natural when responding back.


AWS services/ tools are super easy to use, I guess due to their highly user-friendly environment. You can pick any of these project that I shared today, and go to their official documentation and follow the instructions. They are easy to apply. I think these projects will be very helpful for your resume and future job applications too. Cloud computing services are becoming more and more popular. They are crucial for any business trying to top the charts of digital trends and norms. 

Hoping that you found this article helpful. Thanks for reading!

Let’s connect. Check my Medium blog and Youtube to stay inspired. Thank you,


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