What does CT add to CI/CD for ML?
Why C is the answer
Automatic retraining of models
Continuous training retrains on new data.
In detail
- CT adds automatic retraining.
- CT adds a loop that CI/CD lacks.
Use case
An illustrative scenario, not a real incident.
New data arrives, and the pipeline retrains and evaluates a model automatically.
See it in different ways
flowchart TD A["Code changes"] --> B["CI: test and build"] B --> C["CD: deploy the pipeline"] D["New data or a trigger"] --> E["CT: retrain the model"] E --> F["Model Registry"]
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
mindmap
root(("ML delivery"))
n1["CI"]
n2["Test code"]
n3["CD"]
n4["Deploy"]
n5["CT"]
n6["Retrain models"]Key point: CT automates retraining.
Why the other options are wrong
- A. A new feature store
- It adds no feature store.
- B. A larger serving endpoint
- It does not change serving size.
- D. A faster code review
- It does not change code review.