Can Türk
Program Manager Machine Learning Data
Zürich Metropolitan Area Switzerland
Summary
🤩
Rockstar🎓
Top SchoolCan Türk is a Program Manager in Machine Learning Data based in Zürich with five years of hands-on experience turning data and AI initiatives into business value. He blends technical depth from an ETH Zürich computer science background with product-focused program leadership, having led data and analytics engagements across Europe at Netlight and overseen client projects and teams early in his career with ETH juniors. As an entrepreneur and advisor he has steered startups through digital transformation, and at Apple he now scales ML data programs inside a product-driven environment. He also contributes to open-source tooling as a DevOps engineer on the popular federated learning framework Flower, improving CI, testing and deployment reliability—an indicator of his emphasis on maintainability and automation. Practical, cross-functional, and curious, he thrives at the intersection of engineering, operations and stakeholder alignment to move ML from prototype to production.
5 years of coding experience
2 years of employment as a software developer
Master of Science (MSc) Computer Science, Master of Science (MSc) Computer Science at ETH ZürichAbitur Mathematics and Natural Sciences, Abitur Mathematics and Natural Sciences at Deutsche Schule Istanbul / Alman Lisesi
Turkish, English, German
Stackoverflow
3reputation
116reached
0answers
1question
Github Skills (18)
github-ci10
docker10
python10
dockers10
cicd10
build-automation10
githubaction-workflow10
testing9
application-framework9
poet9
app-framework9
poe9
web-framework9
tensorflow8
bash8
Programming languages (3)
Github contributions (5)
adap/flower
Jul 2020 - Feb 2021
Flower: A Friendly Federated AI Framework
Role in this project:
DevOps Engineer
DevOps EngineerContributions:5 reviews, 19 commits, 23 PRs in 7 months
Contributions summary:Can's contributions primarily focused on improving the build, testing, and deployment infrastructure of the Flower framework. This included upgrading Python versions across various build configurations, integrating linting and formatting tools like Flake8, and adding end-to-end tests within the CI system. They also made changes to Dockerfiles, and build scripts. These changes indicate the user's involvement in ensuring the project's maintainability, reliability, and automation.
federated-analyticsfederated-learning-frameworkmachine-learningkeras-federated-learningflower
BorissowT/SozialeNetzwerke
May 2022 - Jun 2022
aka Data science
Contributions:2 PRs, 13 pushes, 1 branch in 1 month
pythonsciencemachine-learningdata-science
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