Seongyun Byeon
Seoul, South Korea
Summary
🤩
Rockstar🎓
Top SchoolSeongyun Byeon is a data and ML leader with 10 years of experience building production ML and data platforms, coaching teams, and founding a data education company in Seoul. He has led MLOps, data engineering, and team management at SOCAR—designing streaming and ETL pipelines, distributed inference, and operational ML for high-impact products. As founder of 카일스쿨 he teaches practical data skills and coaches data leads and C-levels, translating real-world engineering constraints into action-oriented learning. He was a Google Cloud Champion Innovator and integrated into the Cloud GDE program, reflecting strong expertise in Google Cloud tooling and community leadership. An active practitioner of deployment automation, he contributed MLOps work on a public Boostcamp project—streamlining Docker-based CI/CD and Streamlit model serving. Colleagues know him for combining hands-on technical delivery with structured coaching that scales individual and team capabilities.
9 years of coding experience
8 years of employment as a software developer
경영학, 경영학 at Inha University 인하대학교
Stackoverflow
61reputation
5kreached
0answers
5questions
Github Skills (16)
github-ci10
streamlit10
docker10
mlops10
githubaction-workflow10
serve10
dockers10
cicd10
python9
machine-learning8
pymodbus6
point-cloud-library6
qt6
lidar6
points6
Programming languages (6)
Github contributions (5)
zzsza/Boostcamp-AI-Tech-Product-Serving
Aug 2021 - Jan 2023
부스트캠프 AI Tech - Product Serving 자료
Role in this project:
MLOps Engineer
MLOps EngineerContributions:14 reviews, 46 commits, 78 PRs in 1 year 4 months
Contributions summary:Seongyun primarily contributed to setting up and testing deployment pipelines for a mask classification model. Their work involved configuring Streamlit for model serving and configuring Docker build and deploy processes. Furthermore, the user integrated GitHub Actions for continuous integration and continuous deployment of the application. They also made updates to streamline the deployment process, particularly the use of Docker.
servingmachine-learning-engineermlopsmachine-learning
MLOpsKR/Awesome-MLOps-Contents
Jan 2020 - Mar 2021
Contributions:5 commits, 4 pushes, 1 branch in 1 year 2 months
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