ezkl converts neural networks defined in PyTorch or TensorFlow into ONNX format, then generates ZK-SNARK circuits that can prove computation was performed correctly without revealing model weights or input data. It enables privacy-preserving ML inference where users can demonstrate that a model produced a specific output on specific inputs, without exposing either. The system uses the Halo2 proof system backend and supports verification on Ethereum or in browsers.