In a recent essay, machine learning practitioner Eugene Yan draws seven parallels between ML workflows and personal growth. He argues that the same principles used to build robust models—such as cleaning data, applying transfer learning, and avoiding overfitting—can inform how people approach habits, relationships, and learning.
Yan's core analogy treats life as a modeling problem. Data cleaning becomes a reminder to curate inputs and environments; transfer learning suggests that knowledge gained in one context can be adapted to another; overfitting warns against tailoring one's behavior too closely to a narrow set of past experiences. Ensembling, meanwhile, points to combining diverse perspectives rather than relying on a single approach.
The essay is a single author's perspective rather than a rigorous study, so its value lies in the clarity of the metaphors rather than empirical evidence. Still, it offers a useful framework for reflecting on how technical habits might translate into broader life practices.