Application Progress Favors Same-Distribution Dev and Test Sets

What does ML Yearning recommend when your goal is progress on a specific application rather than research?

Domain adaptation methods are widely applicable and broadly used across most machine learning problems.

Domain adaptation involves training an algorithm on one _____ and having it generalize to a different one.

Match each concept from ML Yearning's domain adaptation discussion to its correct description.

Order the reasoning steps ML Yearning recommends when deciding how to handle differing data distributions in an application project.

How does ML Yearning characterize the scope of applicability of domain adaptation methods?

Choosing dev and test sets from the same distribution makes your ML team more efficient, according to ML Yearning.

ML Yearning recommends choosing dev and test sets drawn from the _____ distribution to efficiently make application progress.

Match each project goal to the strategy ML Yearning associates with it.

Order the key ideas in ML Yearning's argument for preferring same-distribution dev/test sets over domain adaptation in application work.

Contrast domain adaptation research with application-focused dataset strategies.

Evaluate data distribution strategies for a new health tech startup.

Why avoid domain adaptation for specific applications?