In the Fall 2023 edition of CS790/657 Cloud Computing, students did a semester-long team project that allowed them to explore their personal interests in cloud computing. The project also introduced them to the new product development process and in particular, software product development.
An overview of the process they went through is available here.
Below is a list of the articles students wrote to accompany their project results. IMHO the projects are quite innovative and worthy of attention, especially since they represent only about 10 weeks of part-time work done by each team.
Team AI Machine: ML Model Development using Azure Machine Learning Services
The cloud providers offer services for machine learning model development in the cloud, saving you the trouble of setting up adequate resources for model development on premises. This team explored the Azure Machine Learning environment and developed a model using the tool.
Team Asteroid: Electric Vehicle Charging Stations Map
This team recognized the importance of acquiring and conditioning data to create value. They pursued their interest in Extract Transform and Load (ETL) processes and created this application to collect and present publicly-available data on electric vehicle charging stations.
Team Atom: Agile Cart with CI/CD Pipeline
Continuous Integration and Continuous Deployment (CI/CD) processes are a cornerstone of modern software development and DevOps, particularly for cloud applications. This team crafted a simple application to work with, then built a CI/CD pipeline on AWS to gain experience with this critical process.
Team BANScloud: Deep Dive of AWS Elastic Beanstalk – Spring Boot To Do List Application
Elastic Beanstalk is a powerful Platform as a Service (PaaS) service on AWS that simplifies management of production cloud applications by abstracting away the complexity of managing individual AWS resources. This team created a ToDo list application and then dove deeply into how to deploy and manage it on Elastic Beanstalk.
Team Cirrus: AItinerary – Intelligent Travel Itinerary Generation
This team originated an innovative concept to create personalized travel itineraries in the cloud. Their implementation pulls together NLP, ChatGPT integration, Unsplash APIs, AWS Lambda and a React front-end to provide an easy-to-use service on AWS.
Team Cloud 9ers: Smart AI Retrieval Assistant (SARA)
The concept for this project was to develop a Retrieval Augmented Generation (RAG) chatbot for individuals to upload content they own and ask questions of it in natural language format. This team’s AWS implementation combines a vector database, an embedding function for the vector database, and a large language model (LLM) to answer questions.
Team Cloud Chat: Bus Search and Booking API Using Mulesoft and AWS
This team set out to develop an API to search for available buses, book trips, and receive notifications about bookings. To accelerate development, they chose the Mulesoft API development platform and its RESTful API Modeling Language (RAML). They deployed their application on AWS EC2 and RDS.
Team Cloud Crew: Movie Recommendation Chatbot using Amazon Lex
Amazon Lex is a platform service on AWS for creating chatbots that employ conversational AI. This team used Lex to create a movie recommendation chatbot based on data from The Movie Database (TMDB) and employing AWS Lambda. To easily obtain an initial user interface, they integrated their chatbot into Slack.
Team CodewithYash: CloudEduTrack – An Integrated Cloud-Based Quiz Examination System
This project provides an online quiz system for users with motor and visual impairments, featuring a voice-activated interface for enhanced accessibility and a highly customizable user experience. The cloud implementation features a public domain name, AWS EC2, an Apache/MySQL backend and an Angular front-end.
Team Kanya Rasi: Deployment of a Food Delivery System Using IaaS and PaaS
This project comprises a multi-user food delivery workflow in the cloud using the Django web framework. The team gained experience on both IaaS and PaaS by providing two complete production deployments of their application – one on AWS EC2 using NGINX, Gunicorn and MySQL and a second one using the Azure App Service with the same application.
Team Machine Learners: Attendance Log API Using Face Recognition
This application captures attendance information using a live video feed to identify individuals in real time via an AI face recognition module. It also provides for attendance record management. The cloud implementation is on Azure Virtual Machines running a React webapp, a PostgreSQL database, and a face recognition API server.
Team Messengers: WeatherSnap – Meteorological Updates using AWS Simple Notification Service (SNS)
This project demonstrates the use of pub/sub notifications to distribute weather notifications to subscribed users at user-configured times. It sources its data from the Open Weather API and blends several web technologies into a cohesive whole, including an Amazon Lightsail database, serverless functions using AWS Lambda, and the Simple Notification Service (SNS) to provide notifications.