What role do audit logs play in MLOps?

Audit logs are critical in MLOps for ensuring traceability and compliance. They record every change made to the model, data, features, and environments during the ML lifecycle. This allows teams to track who made changes, what changes were made, and why. Audit logs help in diagnosing issues, understanding model performance trends, and providing accountability, especially in regulated industries. They are also useful for facilitating reproducibility of experiments and models, aiding in both debugging and regulatory audits.