A recent guide on Machine Learning Mastery highlights iterators and generators as key tools for memory-efficient processing. Instead of loading an entire dataset into memory, these constructs yield values one by one, which is particularly valuable when working with large files or streaming data in machine learning workflows.
The article cautions, however, that lazy evaluation is not a universal solution. The phrase "Laziness is not a religion" underscores the need for pragmatism: generators can add complexity to code and make debugging harder, and for small datasets the overhead may outweigh the benefits.
Ultimately, the choice between eager and lazy processing depends on the specific use case. Developers should weigh memory constraints against code clarity and performance, applying iterators and generators where they genuinely help rather than as a default practice.