About Me
From customer support to machine learning, building practical ML applications from data to deployment.
Career Transition
My interest in machine learning grew from a passion for data and technology. Through self-study, online courses, and hands-on projects, I learned how to build machine learning solutions from data collection and feature engineering to model training, evaluation, and deployment.
Today, I focus on practical machine learning projects that solve real problems. My portfolio includes a real-time Fraud Detection System and an Exoplanet Host Star Classification project, both deployed through FastAPI and Docker. I enjoy building complete ML applications that combine data science, software engineering, and user-facing solutions.
My goal is to continue growing as a Machine Learning Engineer and contribute to projects where machine learning can create measurable business value.
2025 — Present
Machine Learning Focus
Self-directed learning and hands-on ML projects covering data preprocessing, feature engineering, model training, evaluation, and FastAPI deployment with Docker.
2023 — 2025
Customer Support & Hospitality
Developed strong communication, problem-solving under pressure, and data-driven decision making in fast-paced customer-facing environments.
Focus Areas
Machine Learning Engineering
Built end-to-end ML systems including data preprocessing, feature engineering, model training, evaluation, and deployment.
Production ML APIs
Developed FastAPI inference services for real-time and batch predictions with Docker-based deployment.
Applied Machine Learning
Experience with classification problems, XGBoost, Random Forest, model tuning, cross-validation, and explainability techniques.
Portfolio Projects
Designed and deployed machine learning projects including Fraud Detection and Exoplanet Host Star Classification.