The Development of Garden 🌱: Machine Learning Model Publishing Platform
Garden is a collaborative platform that simplifies the process of publishing and sharing machine learning models. It transforms how researchers publish, discover, and build upon machine learning research by making ML models more accessible and reproducible.
The process was previously command-line-only. Working on the web interface, my primary considerations were how I could make it easier for researchers to publish, manage, and discover machine learning models without technical barriers.
Unlike traditional model repositories that only share individual model files, Garden provides complete ‘ecosystems’ which include data, code, functions, testing, and community collaboration tools, making it significantly easier and faster to build on existing ML research.
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Most ML research is impossible to reproduce due to missing code, data, or environment details. Code and data is also often unavailable or require expensive compute resources, making these models inaccessible to many.
There is also a lack of community: it isn’t easy finding and conecting with others working on similar problems.My Role: Led frontend development and UX design (details here) to create Garden's web interface, transforming complex ML ecosystem management into an intuitive user experience.
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Researched FAIR principles (Findable, Accessible, Interoperable, Reusable) and how they apply to ML model publishing.
Collaborated with rest of the Garden team consisting of fellow developers, designers, researchers, and scientists.
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Designed and implemented feature for editing model metadata.
Designed and built user profile pages enabling researchers to manage their published Gardens and models, as well as basic profile features such as name, email, phone number
Implemented secure login flow using Globus API with Axios interceptors for seamless token management.
Developed reusable component architecture through React, TypeScript, and TailwindCSS.
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I managed to successfully replace the complex CLI workflow with an intuitive and visually pleasing interface! The final product was a scalable React application with efficient state management, a secure authentication system, robust error handling, and user feedback.