Full stack Project Management Dashboard

server for byte's website!

The Kidney Disease Classification project is a comprehensive machine learning pipeline designed to classify kidney disease using advanced tools like MLflow and DVC. It emphasizes reproducibility, scalability, and deployment readiness. The project leverages a modular design, enabling efficient experimentation and deployment in cloud environments.

Terraform module which creates a docker instance resource on AWS.

A full-stack application ( Basic ) demonstrating professional AWS deployment strategies using Node.js, Nginx, and cloud infrastructure. The project provides a comprehensive guide for deploying web applications on AWS EC2, with support for containerization, process management . Key technologies: AWS, React.js, Socket.io, Node.js, Nginx, PM2, Docker.

This repo is developed to perform the sentiment analysis on Arabic tweets using AWS Comprehend and Arsent Dictionary based approach.

How to Replicate the Instance Into Other Region & Take the EBS Volume Backup - EC2 Case Study

In this project, I developed a serverless solution to automate the starting and stopping of Amazon EC2 instances using AWS Lambda and CloudWatch EventBridge. The primary goal was to reduce operational costs by ensuring that EC2 instances are only running when needed.

Frank's + Tonia's + Baron's Ontario University Blog

This is project 3 of the headstarter fellowship. We have built a customized AI chatbot which knows all about the latest population trends.(As of 2024)

Monitoring the CPU utilization of an EC2 instance

Personalized AI discord bot trained on my own messages

Terraform module which creates a docker instance resource on AWS.

This repository focuses on improving the efficiency and sustainability of heavy-duty vehicles by accurately predicting failures in the Air Pressure System (APS) and reducing the cost of unnecessary repairs.

Inspektlabs' AI-assisted technology is quick, easy and 95%-99% accurate in detecting vehicle damage.

The repository showcases a series of exercises and projects focused on big data processing using Hadoop, HBase, Hive, and Spark with Python. Hosted on AWS EMR, these projects demonstrate efficient data handling and processing techniques, leveraging the power of cloud computing to tackle complex data challenges.

Simple Bash scripts for AWS, SMTP, Graphical scripts and more

Built an image recognition pipeline using AWS JAVA SDK. AWS services used were AWS S3, AWS EC2, AWS SQS, AWS Object detection and AWS Object Rekognition.

Terraform module which creates a docker instance resource on AWS.