Introduction: the scaling wall
In 2015, Uber’s growth was outpacing its infrastructure. As the company evolved from a luxury car service into a global logistics powerhouse, every engineering team began building their own machine learning “silos.” Data scientists were spending 80% of their time on manual “plumbing”—managing servers and data pipelines—rather than building models. The “cold start” problem was a reality: it took months to get a single model from a laptop into a production environment.
“Data scientists were…
