PyTorch Conference Europe 2026

Traditional compliance relies on retroactive logs and manually stitched audit trails, while Opacus, CrypTen, and Captum address isolated concerns without providing end-to-end lifecycle traceability. Compliance-as-Code embeds regulatory controls as executable logic within training and inference pipelines, turning compliance into a continuous engineering function and reducing audit costs.

PyTorch’s dynamic execution model enables real-time auditing and compliance gates across the model lifecycle. Features such as the Dispatcher, custom Autograd functions, and the hook system allow logging, constraint checks, and risk controls to be embedded directly into execution. For ex, a fairness gate using training hooks can block model export if disparity exceeds thresholds. Dataset initialization can detect imbalance, while dispatcher-level monitoring generates tamper-resistant audit trails linking data, model versions, and outputs. In deployment, metrics, inference hooks track bias drift, accuracy degradation, and human-intervention counts.

The talk presents practical PyTorch patterns for automated documentation, immutable audit trails, and faster certification in regulated AI deployments.