I work on data driven systems, with experience in data pipelines, analytics platforms, cloud integrations, and CI/CD workflows. I focus on building solutions that are reliable, maintainable, and suitable for real world use.
I am currently pursuing a Master’s in Machine Learning and Mathematical Modelling, developing my skills in machine learning, statistical modelling, and MLOps.
I’m interested in the intersection of data engineering and machine learning, aiming to build systems that move beyond experimentation into practical, production ready applications.
- Strengthening fundamentals in machine learning, statistical modelling, and optimisation
- Building end-to-end ML workflows in Python (data preparation → modelling → evaluation)
- Learning MLOps practices, including model versioning, monitoring, and basic deployment patterns
- Applying cloud and automation principles to data and ML pipelines
- Improving code quality, testing, and overall system reliability
- Data Engineering: Data pipelines, integration patterns, and analytics-ready data models
- Analytics & Monitoring: SQL-based analysis, dashboards, and operational metrics
- Cloud & Automation: CI/CD workflows, containerisation, and cloud platforms
- Machine Learning (Learning & Applied): scikit-learn, PyTorch, model evaluation, and MLOps practices