In a new essay, Eugene Yan writes that he once thought deploying machine learning was difficult—until he had to maintain multiple systems in production. The piece reframes the conventional focus on model building and launch, arguing that sustained operation is a distinct and more demanding phase.
Yan’s post is structured around six little-known challenges that surface after deployment. The excerpt does not enumerate them, but the central takeaway is clear: teams should expect operational issues that are easy to miss when a single model is being shipped, and that multiply when several systems run at once.
Because there is only one source, there are no differing perspectives to compare. The account rests entirely on Yan’s own experience and should be read as a practitioner’s field observation rather than a formal study.