Itay Gabbay is a seasoned technology leader and Co-Founder & CTO with nine years of experience building secure, scalable cloud and ML systems from ideation to production. A Mamram alum with an MSc in Information Systems Engineering from Ben-Gurion University, he has led engineering teams in high-stakes environments including a 100,000-user private cloud at the IDF鈥檚 Cyber Defense Directorate. As VP R&D at Deepchecks he helped shape a leading ML validation platform and contributed code to the popular open-source deepchecks project, adding calibration metrics and robustness fixes. He blends hands-on ML engineering and cloud architecture expertise with product-facing leadership, favoring practical, testable solutions that harden model reliability in production. Based in Tel Aviv, he now applies that blend of security-first infrastructure and ML validation experience to early-stage startup challenges.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at The College of Management Academic Studies
Master of Science - MS Software and information systems engineering, Master of Science - MS Software and information systems engineering at Ben-Gurion University of the Negev
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Role in this project:
ML Engineer & Data Scientist
Contributions:2 releases, 783 reviews, 387 commits in 1 year 3 months
Contributions summary:Itay contributed significantly to the development and maintenance of the "deepchecks" repository, which provides tools and libraries for validating machine learning models and data. Their work focused on implementing new checks, specifically a calibration metric check. In addition to this they have refactored code, addressed issues related to data handling (e.g., handling NaNs), and made improvements to testing methodologies for the project. The user demonstrates a strong understanding of machine learning model evaluation.
Contributions:496 pushes, 1 branch, 32 tags in 1 year 1 month
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