Model Registry

A model registry acts as a single source of truth for all trained model artifacts. It provides immutable storage and semantic versioning (e.g., v1.2.3) for every model iteration, enabling traceability and preventing environment-specific "works on my machine" issues. Key capabilities include:

• Immutable artifact storage for model weights, configuration files, and serialized formats (e.g., .pt, .onnx).
• Version lineage showing the progression from v1.0.0 to v1.1.0.
• Metadata association linking each version to its training code commit, dataset snapshot, and hyperparameters.

This function establishes a complete audit trail by linking a model version to all artifacts and events in its lifecycle. It answers critical questions: Which training run produced this model? What data was used? This is essential for reproducibility, debugging, and regulatory compliance. Lineage typically captures:

• Code Commit Hash: The exact Git commit of the training script.
• Dataset Version: Identifier for the specific dataset snapshot used.
• Experiment Tracking Link: Connection to metrics and parameters logged in tools like MLflow or Weights & Biases.
• Environment Snapshot: The container image or requirements.txt used for training.

Model registries enforce a controlled promotion workflow through distinct lifecycle stages such as Staging, Production, and Archived. This gates deployment based on validation criteria, preventing untested models from reaching users. A typical workflow:

1. A model is registered in the None or Development stage.
2. After passing integration tests, it is promoted to Staging for shadow deployment or A/B testing.
3. Upon meeting performance Service Level Objectives (SLOs), it is promoted to Production for live traffic.
4. Superseded models are moved to Archived.

Beyond the binary artifact, a registry stores rich, searchable metadata that contextualizes the model. This includes technical metadata (framework, signature, input schema), performance metadata (validation accuracy, latency benchmarks), and business metadata (owner, use case, description). This enables:

• Discoverability: Engineers can search for models by accuracy, framework, or creator.
• Compliance: Attaching regulatory documentation or bias assessment reports.
• Informed Deployment: Comparing the latency and accuracy of candidate models before promotion.

As a central system, the registry enforces role-based access control (RBAC) and governance policies across the model portfolio. This ensures only authorized users can promote or modify production models, which is critical for security and auditability. Key controls include:

• Permissions: Defining who can register, read, promote, or delete models.
• Approval Gates: Requiring manual sign-off from a model reviewer or compliance officer for production promotions.
• Audit Logs: Recording every action (who promoted what model and when) for compliance with standards like SOC 2 or the EU AI Act.

A model registry provides essential capabilities for MLOps governance:

• Versioning & Lineage: Tracks every iteration of a model with unique identifiers (e.g., v1.2.3), linking it to the exact training code, dataset, and hyperparameters used.
• Metadata Storage: Catalogs critical information like performance metrics (accuracy, F1-score), training environment, and model signatures (expected input/output schema).
• Stage Management: Manages a model's progression through defined lifecycle stages (e.g., Staging, Production, Archived).
• Access Control & Audit Trail: Enforces role-based permissions and logs all actions (who promoted, deployed, or deleted a model) for compliance.

A model registry interacts closely with other components in the Model Serving Architectures landscape:

• Model Serving: The registry is the source of truth for which model version is approved for deployment to an inference server.
• Model Monitoring: Performance and drift metrics from live endpoints can be fed back to the registry to trigger model retraining or rollback.
• CI/CD Pipelines: Automated pipelines use the registry as a gate; they test a candidate model, and if it passes, register and promote it.
• Feature Stores: While separate, a registry often references the version of features (from a feature store) used to train a model, ensuring consistency between training and serving.

The process of deploying a trained machine learning model into a production environment where it can receive input data, perform inference, and return predictions via a defined interface. A model registry provides the approved, versioned artifacts that a serving system loads and executes.

• Primary Function: Hosts the model as a live service (e.g., REST API, gRPC endpoint).
• Registry Interaction: Pulls specific model versions (e.g., v1.2.3) from the registry for deployment.
• Key Metrics: Latency, throughput, and availability.

A specialized software application designed to load machine learning models, manage computational resources (like GPU memory), and execute inference requests at scale. It is the runtime engine for served models.

• Examples: NVIDIA Triton Inference Server, TorchServe, TensorFlow Serving.
• Registry Dependency: Loads serialized model files (e.g., .pt, .onnx) and associated metadata from the registry.
• Optimizations: Implements techniques like continuous batching and KV cache management to maximize hardware utilization.

The phase of the ML lifecycle where a trained model is integrated into a live production environment. This encompasses the technical steps of packaging, configuration, and release orchestration.

• Process: Involves retrieving a model from the registry, containerizing it with its dependencies, and scheduling it on infrastructure (e.g., via a Kubernetes Deployment).
• Strategies: Uses patterns like canary deployment and blue-green deployment to manage risk.
• Governance: The registry provides an audit trail of which model version was deployed, when, and by whom.

The continuous observation of a deployed model's performance, behavior, and operational health in production. It closes the loop back to the registry by informing decisions to retrain or roll back models.

• Tracked Metrics: Prediction accuracy, latency, throughput, and hardware resource consumption.
• Drift Detection: Identifies model drift (concept drift, data drift) signaling degraded performance.
• Registry Feedback: Monitoring outcomes (e.g., high drift scores) can trigger the registration of a new, retrained model version to replace the underperforming one.

The practice of assigning unique, immutable identifiers to different iterations of a machine learning model. This is a core function provided by a model registry.

• Purpose: Enables precise tracking, reproducibility, rollback, and simultaneous serving of multiple model variants (A/B testing).
• Scheme: Often uses semantic versioning (e.g., MAJOR.MINOR.PATCH) or commit-based hashes.
• Metadata: Each version is linked to training code, dataset snapshots, hyperparameters, and evaluation metrics stored in the registry.

Continuous Integration and Continuous Delivery pipelines adapted for machine learning systems. A model registry acts as the central artifact repository in these pipelines.

• CI: Automatically trains and validates a new model candidate on code/data changes.
• CD: Upon passing tests, the pipeline promotes and registers the new model version, then triggers a controlled deployment.
• Orchestration: Tools like Kubeflow Pipelines or GitHub Actions coordinate training, registry updates, and deployment stages.