Which service builds repeatable ML workflows on AWS?
Why C is the answer
SageMaker Pipelines
Pipelines defines steps for processing, training, evaluation and registration.
In detail
- SageMaker Pipelines builds repeatable ML workflows.
- Runs are recorded for later review.
Use case
An illustrative scenario, not a real incident.
A team defines processing, training, evaluation and registration as steps in SageMaker Pipelines, so each run repeats them.
See it in different ways
flowchart LR
A["Process data"] --> B["Train"]
B --> C["Evaluate"]
C --> D{"Meets the threshold?"}
D -->|Yes| E["Register the model"]
D -->|No| F["Stop and notify"]
E --> G["Deploy after approval"]flowchart LR A["CodeBuild: build the image"] --> B["ECR: store it"] B --> C["Pipelines: train and evaluate"] C --> D["Model Registry: approve"] D --> E["Endpoint"]
flowchart LR S0["Process"] --> S1["Train"] --> S2["Evaluate"] --> S3["Register"]
Key point: Pipelines builds repeatable workflows.
Why the other options are wrong
- A. AWS Config
- Config tracks resources.
- B. SageMaker Ground Truth
- Ground Truth labels data.
- D. SageMaker Model Monitor
- Model Monitor watches models.