The ML field moves very fast. How do you keep current and decide what is worth learning?

How you keep current: papers for the areas you work in, practitioner blogs and library release notes for what is actually usable, and implementing something rather than only reading it. Reproducing a result teaches you the assumptions the paper glosses over.

How you filter — and this is the part that matters:

• Fundamentals over trends. Understanding bias-variance, validation methodology, and how to interrogate data has not changed in decades and determines whether your work is correct. Most production ML failures are methodology failures, not architecture failures.
• Does it fit a problem you have? A new architecture that needs a hundred times the data you possess is interesting, not useful.
• Is it reproducible and maintainable? A published state-of-the-art result with no released code and enormous compute requirements is not a candidate for production.

Note: A strong, honest position: most business ML value still comes from good features, clean data, and gradient-boosted trees on tabular problems — not from the latest architecture. Being able to say that, while still following the field, shows judgement rather than fashion-following.