Kyle Stratis is a VP of Engineering and founder with 12 years of experience building AI, data, and cloud platforms that turn research into production-ready systems. He has a proven track record of radically accelerating R&D—cutting model training from a full day to 15 minutes—by designing automated training pipelines, a feature store, and a model composer that empowered cross-functional teams. A pragmatic engineer and former founder, Kyle blends ML engineering, MLOps, and backend expertise to produce measurable cost and time savings (e.g., 36% training-time and 47% hourly cost reductions at Vizit). He contributes to open-source projects and educational resources, including backend improvements to Habitica and data-focused notebooks for Real Python, reflecting a commitment to community and reproducible workflows. Based in Boston and grounded in a behavioral and cognitive neuroscience MS, he brings an uncommon mix of experimental rigor and product-minded system design to complex AI problems.
12 years of coding experience
11 years of employment as a software developer
Masters of Science (MS) Behavioral and Cognitive Neuroscience, Masters of Science (MS) Behavioral and Cognitive Neuroscience at University of Florida
Bonus materials, exercises, and example projects for our Python tutorials
Role in this project:
Data Scientist
Contributions:3 reviews, 20 commits, 5 PRs in 1 year 4 months
Contributions summary:Kyle added a Jupyter notebook, likely as a bonus material, showcasing examples of combining data with Pandas' `merge`, `join`, and `concat` functions. The notebook includes examples of importing and manipulating data from CSV files and demonstrates various data analysis operations. The user's contribution focuses on data wrangling and analysis using Python and the Pandas library.
A habit tracker app which treats your goals like a Role Playing Game.
Role in this project:
Back-end Developer
Contributions:5 commits, 2 PRs, 15 comments in 17 days
Contributions summary:Kyle primarily focused on improving the backend logic of the Habitica application, specifically related to database queries and data integrity. Their contributions involved modifying MongoDB queries to prevent the accumulation of unwanted items and ensure proper behavior when subscriptions end. They also worked on refining data handling by adding checks for the existence of specific fields, and made formatting changes for enhanced code readability.
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Kyle Stratis - VP Of Engineering at Stratis Data Labs