Principle:Mlflow Mlflow Model Registration
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Model_Management |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Registering a model version in a centralised catalogue creates a governed entry point for promotion, deployment, and auditing across the model lifecycle.
Description
Model registration bridges the gap between experimental model artifacts and production-grade governance. While a logged model lives within the scope of a single experiment run, a registered model is a named, versioned entity in a shared registry that multiple teams and systems can reference. Each time a model is registered under the same name, the registry creates a new version, preserving the full lineage of iterations.
Registration enables lifecycle management patterns that are impossible with run-level artifacts alone. Teams can attach metadata, set version tags, assign aliases such as "champion" or "challenger", and enforce approval workflows before a model is promoted to production. The registry also provides a stable reference scheme -- consumers can load a model by name and version or by name and alias, decoupling deployment configuration from run-specific identifiers.
The act of registration can be explicit (calling a dedicated registration function with a model URI and a name) or implicit (supplying a registered_model_name parameter at logging time). In both cases the outcome is the same: a new ModelVersion record that points back to the underlying run artifact.
Usage
Use model registration after a logged model has been evaluated and deemed suitable for broader consumption. Registration is appropriate whenever a model will be referenced by downstream systems (serving endpoints, batch pipelines, monitoring dashboards) or whenever multiple versions of the same model need to be compared and governed in a single catalogue.
Theoretical Basis
Model registration implements the single source of truth principle for model artifacts. In software engineering this corresponds to a release repository or package registry; in machine learning it serves an analogous role by providing an immutable, versioned catalogue of model binaries and their metadata. The registry pattern also supports the separation of concerns between experimentation (where many models are tried) and production (where only vetted models are deployed), mirroring the distinction between feature branches and release branches in version control systems.