I design, build, and ship production‑grade ML systems end‑to‑end: from data and modeling to APIs, deployment, and monitoring. I focus on measurable business impact, reliability, and clear communication.

• End‑to‑end ML delivery: data pipelines → modeling → evaluation → serving
• Practical deep learning (PyTorch, TensorFlow) and classical ML (scikit‑learn)
• API design and model serving (FastAPI), performance and observability
• Data storage and processing with SQL and Python ecosystems
• Clean code, reproducibility, and experiment discipline

• Languages: Python, C++, R
• ML/DL: PyTorch, TensorFlow, Keras, scikit‑learn, NumPy, SciPy, Pandas, Matplotlib
• Data & Storage: PostgreSQL, MariaDB
• Serving & Backend: FastAPI, REST, batch/offline inference
• Environments: Jupyter, PyCharm, VS Code, RStudio

• Russian (native), English, Spanish

• Credly: https://www.credly.com/users/denis-kolenko/badges
• Kaggle: https://www.kaggle.com/deniskolenko

• LinkedIn: https://www.linkedin.com/in/denis-kolenko/
• X (Twitter): https://x.com/den1ksk
• Email: deniskolenko.work@gmail.com