Recent Projects
User Attendance Microservice API
The User Attendance Microservice API is a web-based platform that allows staff members to register, log in, and mark their attendance, while the admin can manage staff data and view attendance records. The application uses Flask as the web framework, SQLAlchemy for ORM, and Azure SQL for the database. Containerization is done using Docker, and Kubernetes is used for orchestration. Azure DevOps handles the CI/CD pipeline for building, testing, and deploying the API to Azure service.
Azure ETL Data Pipeline Automation
The project aimed to build a dataflow pipeline on Azure Data Factory that will extract data from an Azure SQL database table and load the extracted data into a new Azure SQL table and Azure blob storage in CSV format. After that, I set up a continuous integration continuous delivery process using Git source control and pushed the pipeline to GitHub. The GitHub repository was configured from the Management page on the ADF UX.
Containerized Python Fast-API App for Wikipedia Scrapper
The main aim of this project was to operationalize a Python App from a foundational basis. This simple app uses the fast-API to produce Wikipedia results for any key phrase. This project could be extended to other API microservices or machine learning models. For this project, the GitHub repository is a version control for the application and GitHub Actions to set up the deployment workflow. After the microservice was deployed to the docker hub, it was also deployed to AWS Elastic Container Registry (ECR) for future use.
Automated Students Results’ Processing System
This project is a simple web-based school management application developed for Remedial Science Students at the University of Calabar, Nigeria. The system uses MVC as its architecture. The system was developed using PHP Laravel Framework 5 and other client libraries. This application has two basic functionalities: students and course registrations respectively. Students and courses can be registered on the landing and/or student/course page. The Admin can also perform some CRUD operations on these two modules, while students can keep track of their academic performances on the portal.
Machine Learning Microservice for House Prediction
The main aim of this project is to operationalize a Python flask app that serves out predictions (inference) about housing prices through API calls. In this project, I containerized and deployed a machine learning application using Python, Docker, Kubernetes, and CI/CD Pipeline (CircleCI). Additionally, a pre-trained sklearn model has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.
Employee App CI/CD Automation
In this project, the following objects were covered:
- Explain the fundamentals and benefits of CI/CD to achieve, build, and deploy automation for cloud-based software products.
- Utilized the Blue-Green deployment strategy to design and build CI/CD pipelines that support Continuous Delivery processes.
- Utilized Ansible as a configuration management tool to accomplish deployment to cloud-based servers and further deployed the infrastructure using AWS Cloud Formation.
- For central structured logging and monitoring, Prometheus which is a centralized server error logging, with the Alert Manager was configured to send email alerts to the admin.