In the past few years, the Machine learning (ML) adoption and impact at Uber have accelerated across all business lines. Today, ML plays a key role in Uber’s business, being used to make business-critical decisions like ETA, rider-driver matching, Eats homefeed ranking, and fraud detection.

The Uber Engineering Blog offers insights, technical deep dives, and updates on the engineering challenges and solutions behind Uber's platform and services. Covering topics such as distributed systems, data infrastructure, and mobile development, the blog provides resources for developers interested in large-scale systems and real-world engineering problems. Developers can learn about Uber's technology stack, engineering culture, and best practices for building scalable and reliable systems.

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This post discusses the evolution of Uber's ML platform, Michelangelo, over the past eight years. It highlights the three phases of ML at Uber, the key components of Michelangelo, and the importance of a centralized ML platform.

From Predictive to Generative – How Michelangelo Accelerates Uber’s AI Journey