MLMP

This project presents a decentralized platform for managing and sharing machine learning data using IPFS and web3.storage. The platform focuses on addressing data security, integrity, and accessibility challenges prevalent in the machine learning community.

The system leverages the benefits of decentralized storage, ensuring data remains tamper-proof and clean, which is crucial for reliable model development. By removing single points of failure and censorship risks, the platform guarantees data availability and resilience.

One key aspect of this project is the transparent provenance feature, which allows users to trace dataset origins and history. This fosters trust in the data and ensures users can confidently build upon it without concerns about copyright infringement or potential biases.

In addition to secure data storage, the platform offers a marketplace for users to exchange datasets. This encourages collaboration, knowledge sharing, and joint problem-solving among machine learning practitioners, researchers, and enthusiasts.

This project uses, HTML/CSS and React.js for the frontend, and TailwindCSS for styling. It integrates WorldCoin and Polygon for blockchain functionality and Privacy-Preserving-Proof-Of-Personhood-Protocol (PPPoP) and uses Solidity for smart contract development. The platform connects to decentralized storage through IPFS and web3.storage. Metamask and Rainbowkit serve as browser-based wallets, facilitating transactions.