Felix Altenberger
Bavaria, Germany
Summary
🤩
Rockstar🎓
Top SchoolFelix Altenberger is an MLOps and deep learning engineer with nine years of experience building production-ready ML systems and developer tooling from research prototypes to SaaS. As an early engineer at ZenML he helped transform an open-source CLI into a client-server product, contributing over 250K lines of code and features like stack import/export that improved portability for real-world pipelines. Previously he shipped scalable, real-time size-recommendation models at Presize (acquired by Meta) and has deep academic roots as a TUM PhD student and deep learning instructor focused on 3D body reconstruction. Equally at home in research and engineering, he combines strong ML theory with pragmatic backend and CLI work, and contributes notable fixes and tests across the popular ZenML project. Based in Bavaria, he’s also building real-time avatar technology on GitHub, blending 3D research insights with production MLOps.
8 years of coding experience
3 years of employment as a software developer
UPC School (Universitat Politècnica de Catalunya)Master of Science - MS, Data Engineering and Analytics, 1.2 (GPA 3.8) - top 5% / high distinction, Master of Science - MS, Data Engineering and Analytics, 1.2 (GPA 3.8) - top 5% / high distinction at Technical University of Munich上海交通大学TUM Entrepreneurial Masterclass
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (13)
mlops10
python10
pipeline10
pytest9
cli9
workflow-engine9
machine-learning8
devtools8
ml8
developer-tools8
devops8
apidoc7
api7
Programming languages (4)
Github contributions (5)
zenml-io/zenml
Apr 2022 - Jan 2023
ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:12 releases, 1351 reviews, 422 commits in 9 months
Contributions summary:Felix primarily contributed to improving error messages and refactoring code related to pipeline argument verification within the ZenML codebase. They added unit tests to cover missing bracket pipeline creation scenarios, ensuring the robustness of the pipeline initialization process. Furthermore, the user implemented `zenml stack export` and `zenml stack import` commands, along with their respective CLI functionality, enhancing stack management capabilities and enabling easier sharing and migration of ZenML configurations. The user also addressed error messages for missing return type annotations for `@step` functions.
devops-toolspythonproduction-readytensorflowproduction
karmapramit097/zenml
May 2022 - Jun 2022
ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
Contributions:60 commits in 9 days
pythonzenmldata-sciencedeep-learningproduction-ready
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