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Modulus: An Open Modular Design for Interoperable and Reusable Machine Learning
Salvatore Vivona

Modulus provides an open framework for developers to create modular, inter- operable modules. These modules are designed to be modular (surprise?), reusable and inter-operable locally and remotely via peer to peer communication protocols. Modules are lightweight and general enough to wrap over any machine learning tool. Developers can also organize modules into a module file system, representing their own module hub. Developers can also expose their modules as public endpoints through their local peer, and can restrict access based on their accounts signature. Modulus is by design open source and does not rely on any tokenomics, allowing developers to monetize their public endpoints through any tokenized asset including their own.

Author Information

Salvatore Vivona (University of Toronto)

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