Mini Social Network

Built a Backend for Vercel from Scratch with Node.js, utilizing Redis, AWS S3, ECR, and ECS for scalable cloud infrastructure.

This project helps streamline the visa approval process by using machine learning to predict the likelihood of US visa approval for employees. It can assist immigration officials in making quicker, data-driven decisions, reduce manual work, and enhance accuracy.

Deploying a nextjs app to eks using github-actions ci/cd

AWS Terraform module for a Lambda function with ECR repository and CloudWatch log stream.

A dockerized (containerized) project deployed to ECS using Jenkins pipeline as a code

Terraform for Provisioning AWS ECS with EC2 Launch Type (Demo Included)

NoteSpace: Cloud-native note-taking app with React & AWS. Secure authentication, storage, and serverless backend. Modern UI, scalable infrastructure. Flexibility for personal/professional use. Access notes anytime, anywhere. Simplify note-taking with NoteSpace's intuitive interface.

Terraform module to provision an Amazon Elastic Container Registry.

This is a simple 2-tier microservice web application with backend written in Python (Flask) and frontend written in Nodejs (Express). The frontend microservice serves the http request while the backend microservice fetches and display messages upon clicking any images in the frontend.

Containerized Monitoring App in Python

Repositório para armazenamento de Workflows do GitHub Actions para AWS ECR

Implementation of ML-ops pipeline for US Visa approval prediction application, further deployment on AWS Ec2 instance using Docker, CI/CD tool Github actions.

ActixWeb project template with a lot of basic project must-have features, such as containerization and deployments to GCR, ECR, Dockerhub and regular old VM using Github Actions. Also Unit Test, Code Coverage, Also Static Analysis and Staged Flow (such as dev, staging, uat and production) are part of this project template repository.

Deploying a springboot Java Application into an EKS (Elastic Kubernetes Service) cluster using Helm and a Jenkins Pipeline

The project includes setting up a VPC with private subnets, EC2 instances, RDS, and S3, along with Docker containers for application deployment. It also features a CI/CD pipeline with AWS CodeDeploy and CodePipeline. Live data visualisation using Metabase BI tool.

Implementation of MLops pipeline for Chest Disease Classification from Chest CT Scan Images using computer vision Vgg16 pretrained Image classification model. further perform deployment on AWS EC2 using Docker, CI/CD Jenkins tool, using Flask as front end interface.

Build End to End ML pipeline for USVisa prediction, deploy web App to AWS Ec2 instance using Docker, CI/CD with github actions

Quasar PoC, Multitenant PoC.