Caddle is a IPFS-based data aggregating tool focused towards the ML and Data Science community. It acts as a platform to explore datasets for research and model training across various fields and industries.
Large datasets is crucial for research analytics, training accurate machine learning models etc... Caddle bridges the gap between data aggregators and the data science community by providing a platform to push huge datasets to decentralized cloud and thereby enabling better accuracy for ML models, better results for research analytics and many other such applications.
IPFS' decentralized nature allows for better speeds when considered across the globe and the competetive storage rates offered by services like Web3 Storage enables Caddle to store higher volumes of data at a much cheaper rate. As for these reasons, I integrated Web3 Storage API using it's JavaScript SDK to store and retrieve files from the IPFS nodes. I made use of versioning system, similar to w3.name to version the datasets and maintain the history of edits throughout.
Caddle is built using:
- Next.js
- Tailwind CSS
- Firebase
- IPFS / Filecoin client (web3.storage)