Building Trust in ML - Mapping the Model Lifecycle for ML Integrity and Transparency

This talk presents Atlas, a framework for improving integrity and transparency across the machine learning lifecycle. It demonstrates artifact authentication, hardware-based attestation, and provenance tracking in an end-to-end ML pipeline.