Collaborative Evolution in Machine Learning at Uber

This chapter explores the partnership between Uber's machine learning infrastructure team and the Michelangelo platform, detailing their journey from data scientist embedding to self-service features like PyML. It addresses the complexities of integrating legacy models with new technologies and the challenges of achieving low latency in real-time applications. The conversation also weighs the benefits of using existing platforms against the need for tailored solutions in a fast-paced data environment.