Eugene Yan's latest essay compiles 39 lessons from building and operating ML systems. The lessons span technical topics like production and scaling as well as softer skills around execution, collaboration, and user focus. Because the piece is a single author's retrospective, it offers one consistent viewpoint rather than a debate between sources.
The technical lessons emphasize that ML work extends beyond training models. Production and scaling concerns—such as infrastructure, monitoring, and operational reliability—are central to whether a system succeeds. The non-technical lessons are equally prominent: execution discipline, cross-team collaboration, and designing for actual users are framed as critical to long-term impact.
The essay also includes conference etiquette, a reminder that knowledge sharing and community norms matter in ML careers. With only one source, there are no conflicting findings to compare; the value is in the density of practical advice.