从预测到生成-米开朗基罗如何加速优步的人工智能之旅
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.
在过去几年中,机器学习(ML)在Uber的采用和影响在所有业务线上加速。如今,ML在Uber的业务中发挥着关键作用,用于做出ETA、乘客-司机匹配、Eats首页排名和欺诈检测等业务关键决策。
As Uber’s centralized ML platform, Michelangelo has been instrumental in driving Uber’s ML evolution since it was first introduced in 2016. It offers a set of comprehensive features that cover the end-to-end ML lifecycle, empowering Uber’s ML practitioners to develop and productize high-quality ML applications at scale. Currently, approximately 400 active ML projects are managed on Michelangelo, with over 20K model training jobs monthly. There are more than 5K models in production, serving 10 million real-time predictions per second at peak.
作为Uber的集中式ML平台,Michelangelo自2016年首次推出以来,在推动Uber的ML发展方面发挥了重要作用。它提供了一套全面的功能,涵盖了端到端的ML生命周期,使Uber的ML从业者能够以规模化的方式开发和产品化高质量的ML应用。目前,Michelangelo上管理着约400个活跃的ML项目,每月进行超过20,000个模型训练作业。在生产环境中有超过5,000个模型,每秒提供10百万个实时预测。
As shown in Figure 1 below, ML developer experience is an important multiplier that enables developers to deliver real-world business impact. By leveraging Michelangelo, Uber’s ML use cases have grown from simple tree models to advanced deep learning models, and ultimately, to the latest Generative AI. In this blog, we present the evolution of Michelangelo in the past eight years with a focus on the continuous enhancement of the ML developer experience at Uber.
如下图1所示,ML开发者体验是一个重要的乘数,使开发者能够产生实际的商业影响。通过利用Michelangelo,Uber的ML用例已经从简单的树模型发展到先进的深度学习模型,最终发展到最新的生成式AI。在本博客中,我们将重点介绍Michelangelo在过去八年中的演变,着重介绍Uber对ML开发者体验的持续改进。
Figure 1: ML Developer Experience is a multiplier for delivering ML business impact.
图1:ML开发者体验是提供ML业务影响的乘数。
Presently, Uber operates in over 10,000 cities spanning more than 70 countries, serving 25 million trips on the platform each day with 137 million monthly active users....