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When designing a multi-stage system instead of an end-to-end model, assess whether enough relevant training examples can be collected for each proposed learned stage. A stage whose required data is too difficult or costly to obtain may be impractical, while non-learned stages do not require this training-data check.

Choose Pipeline Stages You Can Train

If you have a large labeled dataset for the intermediate parts of a system, it can make sense to build the solution in stages. The extra data can be used to train each intermediate component well, which may make a multi-stage pipeline more practical and effective than trying to learn everything in one model.

When Abundant Labels for Substeps Support a Staged Design

What factor should guide how you divide a modular prediction system into components?

Labeled data access can shape component choices in a modular system.

Choose components where training data can be collected _____.

Match each modular-system term with its meaning in pipeline planning.

Question:
In a non-end-to-end system made of separate modules, explain how the ease of obtaining training examples should influence which module design you choose.

Sample answer:
For a pipeline built from separate components, each module should be judged partly by whether its own training examples can be obtained in practice. If the data needed to train one candidate module can be gathered with little difficulty, that module is more attractive. If the necessary data would be expensive or impractical to label or collect, that weakens the case for using it. So data availability is a selection criterion for each module, not just for the system as a whole.

Key points:
- The design is a multi-component pipeline rather than a single end-to-end model.
- Each candidate module needs its own training data.
- Easier data collection makes a component more practical.
- Hard-to-collect data lowers a component’s appeal.

Rubric:
A strong response should state that, in a pipeline with separate parts, the training data required by each part must be considered individually. It should connect ease of data collection to whether a component is a good choice.

Why training-data availability should affect pipeline component choice

Case context:
A team is building a multi-step prediction system and is deciding between two modules. It can gather training examples for Module Red with little effort, but collecting training examples for Module Blue would take substantial work.

Question:
Using only the stated selection criterion, which module should the team prefer, and what should it conclude about Module Blue?

Sample answer:
The team should prefer Module Red because its training data is easy to collect. Under this criterion, Module Blue is the weaker choice because the data needed to train it is hard to obtain.

Key points:
- Prefer Module Red under the stated criterion.
- Module Red has easy-to-collect training data.
- Module Blue is disadvantaged by difficult data collection.
- The comparison is about a multi-step pipeline, not a fully end-to-end system.

Rubric:
The response should select Module Red, state that easy training-data collection is the reason, and describe Module Blue only in terms of its poorer training-data availability.

Select a pipeline component using training-data availability.

Question:
In a multi-stage system, what data-related check should guide the choice of a separate component instead of an end-to-end design?

Sample answer:
For each proposed step, ask whether you can gather enough relevant examples without major effort to train that step well. If the needed data is hard to obtain, that component may be a poor choice.

Key points:
- Look for enough relevant examples to train the step.
- Judge the effort required to obtain those examples.
- Apply the check to every candidate step.

Rubric:
Full credit requires mentioning both the practical availability of training data and that the check must be done for each proposed component.

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