Building production-ready ML systems using Python, focusing on clean architecture, modular pipelines, and scalable deployment. I actively use Docker for containerization and GitHub Actions for CI/CD to automate testing, training, and deployment workflows. My current interests lie in MLOps, model reproducibility, and deployment optimization.
MLOps-driven projects, scalable ML systems, and real-world applications where models move beyond notebooks into production using Dockerized environments and automated pipelines.
Advanced MLOps strategies, optimizing CI/CD workflows for ML projects, improving model monitoring in production, and designing efficient Python-based architectures for scalable systems.
End-to-end MLOps workflows, experiment tracking, container orchestration concepts, infrastructure automation, and best practices for deploying ML systems using Docker and GitHub Actions.
Python for ML systems, structuring ML repositories, Git workflows, Dockerizing ML projects, CI/CD automation with GitHub Actions, feature engineering, and model evaluation strategies.
"When I am tired, I go to sleep."
