Image Segmentation Using Particle Swarm Optimization & K-means Clustering Algorithm

Python Flask Application Containerized with GitHub Actions and pushed to Docker Hub with GitHub Secret Actions credentials.

This project demonstrates a complete workflow for developing, testing, and deploying an AI model. The primary objective is to build and test a machine learning model, expose it via a REST API, and automate the entire process using CI/CD pipelines. Below is a detailed summary of the work done.

Dockerizing simple django application

Python backend powered by FastAPI and MongoDB for seamless course info management. Rigorously tested with Pytest, and Containerized for effortless deployment across systems.

Dashboard for Amharic Speech to Text translation

Vanilla DRF Structure. Use an 'Article' model to practice the API CRUD. Explaining the function-based-view, class-based-views & generic-views-mixins by using them on the same CRUD operations for articles. Dockerize the project into the "dockerization" branch. The containerized version of this project is deployed in a heroku container.

Bookstore Web API Application aims to create a bookstore web service using Docker to have the understanding to dockerization of an application. The application code is to be deployed as a RESTful web service with Flask using Dockerfile and Docker Compose on AWS Elastic Compute Cloud (EC2) Instance using AWS CloudFormation Service.