Martin Chovanec is a pragmatic software developer with 11 years of experience building backend and platform solutions, combining a strong academic foundation in software engineering and AI from Czech Technical University and USI. He specializes in Java, Python and ServiceNow, delivering integrations and production features for clients including DHL, Siemens Mobility and international banks while often tackling legacy technical debt and turning monoliths into testable units. As a self-employed consultant he blends hands-on implementation with technical analysis and documentation for transport and logistics systems, favoring innovative, quality-driven and sustainable solutions. An active open-source contributor, he improved TensorFlow integration in the well-known Sacred experiment-tracking library, exposing an attention to reproducible ML workflows that complements his enterprise software work. Based in Prague, he is currently not seeking a secured role but remains engaged in impactful projects and exploratory learning across many tech areas.
11 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at Czech Technical University in Prague, Faculty of Information Technology
Master's degree, Computer Software Engineering, Distinction (~Cum Laude), Master's degree, Computer Software Engineering, Distinction (~Cum Laude) at Czech Technical University in Prague
Information Technology, Information Technology at Soukroma stredni skola vypocetni techniky (Private high school of IT)
Artificial Intelligence, Artificial Intelligence at USI Università della Svizzera italiana
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Role in this project:
Back-end Developer & ML Engineer
Contributions:45 commits, 7 PRs, 10 comments in 2 years
Contributions summary:Martin primarily contributed to the development of the Sacred library's integration with TensorFlow. They implemented functionality to track and log TensorFlow summary writer paths, allowing users to monitor experiment metrics within the Sacred framework. This involved creating a decorator and context manager to automatically capture and store the log directories of TensorFlow's FileWriter instances. Additionally, the user updated the tests and documentation to reflect changes and improvements in the TensorFlow integration.
Contributions:94 commits, 75 pushes, 1 branch in 11 months
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Martin Chovanec - Software Developer at Self-employed