Model Registry Governance: Moving Beyond Simple S3 Weight Storage

Storing model weights as timestamped .pt or .onnx files inside an unmanaged cloud bucket is the modern equivalent of deploying production web applications via raw FTP. When a regression occurs in production, finding the exact code commit, training split, and hyperparameter configuration becomes a forensic nightmare.

What True Model Provenance Requires

A production-grade model registry is not merely a file storage system; it is an immutable ledger of machine learning assets. Every registered artifact must link irrevocably to four critical metadata pillars:

1. Dataset Invariant: The exact cryptographic hash of the training, validation, and test datasets used during the experiment.
2. Environment Manifest: The pinned lockfile of dependencies, base Docker image digest, and CUDA runtime version.
3. Evaluation Benchmark Scorecard: Quantitative evaluation metrics computed against fixed holdout benchmarks under reproducible test conditions.
4. Signed Promotion Audit: An explicit log recording which automated gate or engineer authorized promotion from Staging to Production.

Automating the Promotion Lifecycle

Rather than permitting ad-hoc manual tagging, implement programmatic promotion gates inside your CI/CD pipeline. Candidate models should only achieve 'Production' status after passing automated regression tests, latency SLAs, and bias-variance threshold checks.