EZKL is an engine for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML).
It is a compiler for making zero-knowledge (zk) circuits from common computational libraries like pytorch and tensorflow so that you can think about end use-cases instead of circuit design.
It is a suite of tools that mean you can deploy these zk-circuits in less than a minute and integrate them into browser apps and on-chain apps painlessly.
It is a tool for moving complex on-chain computations off chain in a verifiable fashion using zk.
It is a community of dedicated open-source collaborators across many groups and organisations.
The project was founded by Jason and Dante as an exploration in both privacy-preserving machine learning and as method for bringing machine learning models on-chain.
