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Machine Learning Operations

MLOps Pipeline

A lightweight real-time machine learning pipeline that collects user input, stores data, retrains models, and serves predictions as new data arrives. It combines scikit-learn kernel density estimation with a PyTorch-based neural network density regressor, with FastAPI orchestrating the end-to-end workflow. Try the demo below!

Workflow

User inputData point
DjangoWeb application
FastAPIML API
SQL databasePersisted data
FastAPI engineOrchestrate Training
ML enginescikit-learn + PyTorch
DjangoClient updates
PlotlyLive visualization

Docker · Uvicorn · VPS

Input points

Drop points on the canvas and compare a kernel estimator with a neural density model.

Checking the density service…

Clear canvas

Kernel density

Neural density

Berk Can Özmen

+49 xxx xxx xxxx | mail@bcozmen.com | bcozmen | bcozmen.com
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Summary

Machine learning engineer and software developer specializing in building end-to-end ML systems and applications. Strong background in Python, PyTorch, deep learning, computer vision, reinforcement learning, and scientific computing.

Skills

Programming: Python, C++, JavaScript, SQL
Machine Learning: PyTorch, Scikit-learn, Deep Learning, Computer Vision, Reinforcement Learning
Sci. Computing: NumPy, SciPy, Pandas, GeoPandas, Numba, CUDA, Matplotlib, Plotly
Backend: Django, FastAPI, REST APIs, PostgreSQL, SQLite, Celery
MLOps: Git, Docker, Linux, Uvicorn, TensorBoard, Testing, Cloud Deployment
Languages: English (C2), German (C1), Turkish (Native)

Experience

06/2025 - Present
Berlin
Freelance ML Engineer
Self-Employed
  • Develop web applications, full-stack machine learning pipelines, and data analysis tools for clients across various industries.
  • bcozmen.com/autocomplete Code and text autocomplete service
    Python · Django · FastAPI · Qwen · Docker · Cloud Deployment
  • bcozmen.com/mlops Full-stack machine learning pipeline for kernel and neural density regression
    Python · Scikit-learn · PyTorch · FastAPI · Docker · PostgreSQL · Cloud Deployment
  • bcozmen.com/rag Wikipedia RAG system
    Python · FastAPI · Qwen · Cloud Deployment
09/2023 - 09/2024
Berlin
Machine Learning Engineer
Fraunhofer SIRIOS
PyTorch · TensorBoard · OSMnx · Geopandas · Wetterdienst
  • Adapted a geospatial emergency response simulation system into a reinforcement learning environment, modeling Berlin's road network, empirical traffic patterns, and incident data.
  • Designed and implemented RL-based control policies that found the optimal solution for analytically solvable cases and produced solutions within emergency response guidelines for complex cases.
  • Designed the reward scheme and evaluation framework to validate policy performance across both scenarios.
09/2021 - 04/2023
Berlin
Computer Vision & Robotics bcozmen.com/scioi
Science of Intelligence
C++ · OpenCV · ROS
  • Collaborated within a research team to develop an autonomous robotic system combining a camera, robotic arm, and soft hand to solve a lockbox puzzle.
  • Implemented a computer vision pipeline that leverages the robotic arm's movement to generate 3D reconstructions and build a model of the environment.
  • Designed feedback loops between the robotic arm and the computer vision pipeline, enabling intelligent exploration of the environment and adaptive interaction based on observations of the lockbox puzzle.
2019 - 2021
Berlin
Internship at Neural Information Processing Group
Technische Universität Berlin
Numba · NumPy · DEAP · neurolib
  • Implemented a simulation environment for FitzHugh-Nagumo based resting-state brain network models, using empirical structural connectivity and functional connectivity data.
  • Explored the parameter space of the FitzHugh-Nagumo model using genetic algorithms and Bayesian optimization with Gaussian processes.
09/2017 - 04/2019
Berlin
Software Developer
Ten8
Selenium · BeautifulSoup · requests
  • Developed and maintained the company's website and internal application for a Berlin-based startup.
  • Developed a web scraping tool to collect and process initial coin offering (ICO) data, automatically alerting the team to new and potentially interesting opportunities.
2013 - 2016
Istanbul
FRC Team Member & Leader
Inanc Mechatronics Club
  • Designed and built competition robots for the FIRST Robotics Competition (FRC), serving as team leader in 2015 and managing the technical team of 10 members.
  • Built robots entirely in-house from design to competition, performing 3D CAD design, welding, machining/milling, mechanical assembly, and programming for both teleoperated and autonomous operation.

Projects

2024 - Present
Deeppy bcozmen/deeppy
PyTorch · TensorBoard
  • Modular deep learning training framework designed for fast prototyping and experimenting
  • Clean separation of model, data, optimizer, scheduler and training logic with automated logging, checkpointing, automatic mixed precision and other efficiency features
  • Clean separation of GPU and CPU space with automatic device placement, memory management and buffer support for large datasets
  • Developed with feedback from practical experience, including my own work and master thesis.
2022 - Present
Agent-Based Neuro-Evolution bcozmen/abne
PyTorch · CUDA
  • Long-term personal research project investigating neuroevolution in agent-based environments without an explicit loss function.
  • Developed a physics-based environment in which agents evolve neural network controllers to survive and adapt to their environment.
  • Investigated low-dimensional parameterizations of agent behavior using message-passing graph neural networks, along with non-destructive crossover and mutation operators for neural network controllers.
  • Aims to understand the emergence of complex behaviors in a multi-agent system and how the selection pressure drives novel strategies that cannot be designed through explicit loss functions.
2025
r-hash-SANE bcozmen/r-hash-SANE
Deeppy
  • A transformer-based rotation-equivariant neural network for learning NeRF latent spaces.
  • Extension of the SANE architecture to hash-grid NeRFs

Education

2026
Berlin
M.Sc. Computer Science, 1.3 (3.8/4.0) bcozmen/master_thesis
Technische Universität Berlin
  • Thesis: Interpreting NeRF Weights Across Architectures with Graph Meta-Networks for Geometry and Appearance Attribution
  • Supervisor: Prof. Dr. Olaf Hellwich
2021
Berlin
B.Sc. Computer Science, 1.7 (3.5/4.0) bcozmen/bsc_thesis
Technische Universität Berlin
  • Modelling Functional Connectivity in Health and Psychiatric Disorders | Thesis Grade: 1.3
  • Supervisor: Prof. Dr. Klaus Obermayer