- From Research to Production: I spent years analyzing terabyte-scale data in experimental particle physics at CERN. Now, I translate that rigorous analytical mindset into scalable Machine Learning solutions.
- Current Focus: Building end-to-end ML pipelines using APIs and Docker with a focus on efficient MLOps and cloud deployment.
- Efficiency: Using modern tooling like
uvto build the fastest, most reproducible environments possible. - Learning: Advanced system architecture, Kubernetes, and Large Language Model (LLM) orchestration.
- How to reach me: [https://www.linkedin.com/in/oguz-guzel/]
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Synthific – AI Synthetic Data Generator: A zero-cost, high-speed synthetic data generation platform using Google Gemini API (
gemini-3.1-flash-lite) for converting natural language prompts into structured tabular data schemas and@faker-js/fakerfor millisecond-scale bulk row synthesis. Built with Next.js 14 (App Router), TypeScript, and Tailwind CSS, automatically deployed via Vercel CI/CD. Check out the live app https://synthific.vercel.app. -
FastAPI XGBoost deployment: End-to-end machine learning pipeline: training an XGBoost anomaly detection model and serving it via FastAPI and Docker; source code and live link at https://github.com/Oguz-Guzel/fastapi-xgboost-deployment
- AI Agents Fundamentals on Hugging Face (certificate)
- Introduction to SQL on SoloLearn (certificate)
