Model Registry

Immutable versioning captures the exact provenance of every model artifact. The registry links each version to its originating training dataset, hyperparameters, and source code commit hash. This creates a tamper-proof audit trail, enabling teams to instantly roll back to a previous version if model drift is detected in production. It answers the critical question: 'What exact configuration generated this prediction?'

Models progress through defined maturity gates such as Staging, Production, and Archived. The registry enforces strict promotion policies, preventing an experimental model from being accidentally deployed to a Manufacturing Edge AI node. Each stage transition can trigger automated CI/CD pipelines, running integration tests and shadow mode deployments before a model is promoted to control a physical process.

Beyond the binary artifact, the registry stores structured metadata tags. This includes model architecture (e.g., ResNet-50), evaluation metrics (mAP, F1-score), and intended use case. Engineers can attach free-form notes explaining trade-offs. This semantic layer allows for rapid filtering—for example, instantly finding all quantized models optimized for a specific Neural Processing Unit target.

The registry acts as a bridge to the Model Serving Runtime by packaging models with their serving dependencies. It stores ONNX Runtime configurations, container environment files, and feature transformation logic from the Feature Store. This ensures that the exact preprocessing graph used during training is bundled with the model, eliminating the training-serving skew that plagues edge inference.

Role-based access control (RBAC) governs who can register, test, or approve models. Data scientists may have staging write access, while only a Plant Manager or automated Safety Integrity Level (SIL) validator can approve a model for production deployment. The registry integrates with Trusted Platform Modules (TPMs) to sign artifacts, ensuring that only cryptographically verified models are loaded onto edge nodes.

The registry generates a complete, time-stamped audit log of all actions, from initial registration to deprecation. This is critical for regulated industries requiring Algorithmic Explainability. It automatically cross-references deployed models against known vulnerability databases and can enforce policies like 'no model older than 90 days without a retraining trigger,' ensuring continuous compliance with internal governance standards.