Building 'full-stack' applications with both front- and back-end
Experience building robust backend- and desktop-applications using C# and .NET
Creating fully automated machine learning pipelines, optimizing the AI workflow
Training and integrating AI models using Python, OpenCV and many other ML tools
Containerizing and Orchestration for scalable development and deployment
This is a technical training project designed to practice modern .NET application architecture using Clean Architecture, CQRS with MediatR, and microservices with Azure Service Bus
Developed a complete NLP-based sentiment classification system powered by an MLOps pipeline with Kubeflow, MLflow, and MinIO. Deployed and served as a scalable ML system on Kubernetes.
A very small side-project I created during my internship, practicing Docker and Kubernetes. Shorten your URLs on this platform.
