Collaborated on developing an intuitive web platform for the "Data Science @ Georgia Tech" club that democratizes machine learning model creation. The platform allows users to build custom ML models through a visual drag-and-drop interface without requiring extensive programming knowledge.
Technical Achievements:
- Built a responsive React frontend with drag-and-drop functionality for model architecture design
- Created Flask API endpoints to handle model training, evaluation, and data preprocessing
- Implemented AWS S3 integration for efficient file storage and retrieval
- Improved file read/write operations by 14% through optimized cloud storage architecture
- Designed custom preprocessing features with 16 configurable parameters
Impact: The platform serves over 1000 users in the Georgia Tech community, making machine learning more accessible to students across different technical backgrounds. The intuitive interface bridges the gap between theoretical ML concepts and practical implementation.