Juan Gao
数据工艺工程师 at Baidu Inc.
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolJuan Gao is a data engineering specialist with six years of experience building production-ready ML tooling and deployment pipelines at Baidu in Beijing. He focuses on making models more usable and deployable, evidenced by contributions to the popular PaddlePaddle/PaddleClas repository where he fixed dygraph model export, resolved inference bugs, and enabled PaddleHub integration. Comfortable at the intersection of back-end engineering and machine learning, he brings practical experience improving model export and inference reliability for visual classification systems. A Wuhan University alumnus, Juan combines strong institutional experience with hands-on open-source work that streamlines the path from research models to served applications.
6 years of coding experience
Wuhan University
Github Skills (7)
continuous-deployment10
paddlepaddle10
computer-vision10
python10
ml-deployment10
image-classification10
tensorflow3
Programming languages (5)
Github contributions (5)
PaddlePaddle/PaddleClas
Sep 2020 - Jan 2023
A treasure chest for visual classification and recognition powered by PaddlePaddle
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 410 reviews, 649 commits in 2 years 4 months
Contributions summary:Juan primarily contributed to the `tools/export_model.py` file, fixing it to support dygraph models and improving the model export process. They also updated the `tools/infer/infer.py` file, resolving a bug regarding inference, and made changes to support the integration of the model with PaddleHub, including modifications to `deploy/hubserving/clas/module.py`. The user's work focused on improving model usability and deployment, particularly in the context of visual classification and recognition tasks.
image-recognitionknowledge-distillationpaddleimage-classificationswin-transformer
TingquanGao/benchmark
Apr 2022 - Jul 2023
Contributions:83 pushes, 7 branches in 1 year 3 months
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