What should a pipeline check before it deploys a model?
Why D is the answer
That the data and the model pass validation
A pipeline should check that the data and the model pass validation before it deploys.
In detail
- Validate the data and the model before deploying.
- Automate this gate.
Use case
An illustrative scenario, not a real incident.
A pipeline stops when a new model scores below the current one, so it never ships.
See it in different ways
flowchart LR A["Validate data"] --> B["Train"] B --> C["Validate model"] C --> D["Register"] D --> E["Deploy"]
sequenceDiagram participant B as Cloud Build participant P as Agent Platform Pipelines participant R as Model Registry participant E as Endpoint B->>P: Runs after a change P->>P: Validate data and train P->>R: Register if it passes R->>E: Deploy E-->>P: Retrain on a threshold
flowchart TD
Q{"Do data and model pass?"}
Q -->|"Yes"| A0["Deploy"]
Q -->|"No"| A1["Stop the pipeline"]Key point: Validate before deployment.
Why the other options are wrong
- A. That the training job used the newest SDK
- SDK version says nothing about quality.
- B. That the bucket uses a certain storage class
- Storage class says nothing about quality.
- C. That the pipeline ran on a GPU
- A GPU says nothing about quality.