What dependency rule governs a DAG-ordered machine learning pipeline?
Question: In a machine learning pipeline arranged as a Directed Acyclic Graph, what rule limits how one stage may use information from another stage?
Sample answer: Each later stage may use only outputs produced by earlier stages, and it may not feed information backward in a way that creates a cycle. This lets the pipeline be evaluated in a fixed topological order.
Key points:
- Later stages can consume only earlier outputs.
- Backward links that create cycles are not allowed.
- The stages can therefore be computed in a fixed topological order.
Rubric: Response should clearly state that later components may depend only on outputs from earlier components and that the pipeline must remain acyclic so it can be computed in order.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
Which pipeline structure supports error attribution across stages?
Reordering two neighboring stages in a pipeline can still leave the dependency graph valid, and the error analysis remains useful.
Dependency direction in a DAG pipeline
Match each graph property to its role in tracing errors through a model pipeline.
Put the checks in order for deciding whether a workflow graph can be evaluated from left to right.
Reordering Two Stages in a Pipeline
A directed acyclic graph for a pipeline always has only one valid topological order.
Pipeline stages arranged as a DAG can be computed in a fixed _____ order.
Match each idea in DAG-based pipeline error analysis with its best description.
Put the reasoning steps in order for why swapping independent nodes in a DAG still supports valid error attribution.
Why can two independent steps in a directed workflow be reordered without breaking error attribution?
Assessing a component swap in a warehouse robot pipeline
What dependency rule governs a DAG-ordered machine learning pipeline?