ML Engineer with Omdena's international Liberia chapter, January to April 2024.
What I built
- Built and tuned XGBoost models for malaria-incidence prediction.
- Deployed via Streamlit, containerized with Docker for scalable distribution.
- Collaborated internationally across an open-source ML team.
Stack
Python · XGBoost · Streamlit · Docker · scikit-learn.
Why this is on the portfolio
International collaboration experience + work outside the for-profit lane. Demonstrates the ability to ship an end-to-end ML pipeline in a distributed-team setting.
Try it at malaria-prediction.streamlit.app.