ml-audit-repo

You can verify ML repository reproducibility by running staged audits that check executability, data reality, training authenticity, and metric validity, generating verdicts and evidence bundles for pre-release validation.

Evidence-driven ML repo verification is the process of systematically auditing code, data, training, and metrics artifacts to prevent unverified claims from reaching production, producing verifiable artifacts like findings JSON and audit reports.

Point the audit process at a repository path and select an audit level to generate a complete evidence bundle, verifying training authenticity and metric validity to support pre-merge checks.

Performing an ML model audit requires structured artifacts such as run manifests, predictions, and requirements to successfully evaluate claims and generate verifiable findings.

Yes, ML repo verification supports forensic level audits alongside quick and standard levels, allowing teams to enforce traceable provenance and deep validation for high-stakes model deployments.

The best way to enforce traceable provenance is conducting systematic audits of code and data artifacts, generating human-readable audit reports and evidence manifests that verify dataset reality and training authenticity.