I am an MLOps Engineer specializing in building end-to-end, production-grade machine learning systems.
My focus is on automation, reproducibility, scalability, and operational excellence across the ML lifecycle.
- End-to-end MLOps pipeline design
- Model lifecycle management
- CI/CD for ML systems
- Infrastructure orchestration
- Production deployment & monitoring
- Python
- Scikit-learn
- MLflow (tracking, registry)
- DVC (data & model versioning)
- FastAPI
- RESTful ML microservices
- Docker
- Kubernetes
- CI/CD (GitHub Actions)
- Linux environments
- Data versioning with DVC
- Model training & experiment tracking via MLflow
- Model packaging as FastAPI service
- Containerization using Docker
- Deployment and scaling on Kubernetes
- Automated CI/CD pipelines
A production-ready ML system demonstrating the full ML lifecycle.
Highlights:
- Random Forest model (Scikit-learn)
- MLflow for experiment tracking & model registry
- DVC for dataset and artifact versioning
- FastAPI inference service
- Dockerized microservice
- Kubernetes-based scalable deployment
- CI/CD automation
This project mirrors real-world MLOps engineering practices used in production environments.
- Production reliability over experimentation-only ML
- Infrastructure-as-code mindset
- Monitoring, retraining, and reproducibility
- Strong collaboration between ML and DevOps workflows
- GitHub: https://github.com/mauseoluwasegun
- LinkedIn: https://www.linkedin.com/in/mause-iroko-510921323/
⭐ If you find my work useful, consider starring or forking the repositories.