Breaking into machine learning is hard, but staying current after you land the role is a different challenge. The field moves quickly, and the habits that got you hired won't necessarily keep you growing. One approach is to anchor your learning in concrete projects: pick problems that require techniques you haven't used, then build and ship them. That forces you to grapple with messy details that tutorials skip.
Another pillar is keeping up with the literature. Papers, engineering blogs, and conference talks expose you to emerging methods before they become mainstream. But reading alone isn't enough. Writing about what you learn—through blog posts, notes, or even internal docs—forces you to clarify your thinking and creates a record others can benefit from.
The source does not claim there is a single best way; instead, it treats learning as a habit to be cultivated, not a task to be completed. The emphasis is on doing, sharing, and iterating, rather than accumulating certificates or watching more lectures.