Why register a model?
Why B is the answer
It gets a version and traceable metadata
Registration tracks versions and metadata, which supports lineage and governance.
In detail
- Registration gives a model a version and traceable metadata.
- Registering does not deploy anything by itself.
Use case
An illustrative scenario, not a real incident.
A team registers each model, so it has a version and metadata, and anyone can trace what is in production.
See it in different ways
flowchart LR A["Train"] --> B["Track with MLflow"] B --> C["Register a version"] C --> D["Deploy"] D --> E["Monitor"] E -->|"Threshold exceeded"| A
stateDiagram-v2 [*] --> Trained Trained --> Registered: version and lineage Registered --> Deployed Deployed --> Monitored Monitored --> Retrained: drift alert Retrained --> Registered Deployed --> Archived: replaced
flowchart LR S0["Training run"] --> S1["Register model"] --> S2["Version and metadata"] --> S3["Deploy later"]
Key point: Registration gives versions and traceable metadata.
Why the other options are wrong
- A. It cannot be deleted by anyone afterward
- Registered models can be deleted or archived.
- C. It trains faster on the next run
- It has no effect on training speed.
- D. It deploys itself to production straight away
- Deployment is a separate step.