Thursday, January 10, 2019

Michelangelo: Uber's machine learning platform



Uber Engineering is committed to developing technologies that create seamless, impactful experiences for our customers. We are increasingly investing in artificial intelligence (AI) and machine learning (ML) to fulfill this vision. At Uber, our contribution to this space is Michelangelo, an internal ML-as-a-service platform that democratizes machine learning and makes scaling AI to meet the needs of business as easy as requesting a ride.
Michelangelo enables internal teams to seamlessly build, deploy, and operate machine learning solutions at Uber’s scale. It is designed to cover the end-to-end ML workflow: manage data, train, evaluate, and deploy models, make predictions, and monitor predictions. The system also supports traditional ML models, time series forecasting, and deep learning.
Michelangelo has been serving production use cases at Uber for about a year and has become the de-facto system for machine learning for our engineers and data scientists, with dozens of teams building and deploying models. In fact, it is deployed across several Uber data centers, leverages specialized hardware, and serves predictions for the highest loaded online services at the company.

In this article, we introduce Michelangelo, discuss product use cases, and walk through the workflow of this powerful new ML-as-a-service system.

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