preface
We’ve been fortunate to work in machine learning (ML) during one of the most exciting periods in technology. The field is evolving at a breathtaking pace—from breakthrough research to practical applications that touch billions of lives. Being part of this transformation, watching ML systems go from research papers to production services that power real businesses, has been nothing short of remarkable.
The three of us—Benjamin, Shanoop, and Varun—all started our careers as software engineers. We didn’t set out to become ML engineers; we stumbled into it. In our respective organizations, we each found ourselves tasked with taking ML models from notebooks to production. We quickly discovered that while our software engineering backgrounds were invaluable, production ML required an entirely new set of skills and practices.
Our first production deployments were humbling experiences. Models that performed beautifully during training struggled in production. Systems broke in unexpected ways. We found ourselves navigating a fragmented landscape of tools, trying to figure out which ones actually worked for real-world problems. Through trial and error, late-night debugging sessions, and learning from our mistakes, we gradually developed an understanding of what it takes to build reliable ML systems.