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Which change can reasonably improve computational efficiency in an ML workflow?
Increasing dataset size without optimization.
Using batching, parallel preprocessing, multi-core training, or GPU acceleration when appropriate.
Running the same slow code repeatedly without measurement.
Removing all documentation from the project.
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