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cover of episode Inside Uber’s AI Revolution - Everything about how they use AI/ML

Inside Uber’s AI Revolution - Everything about how they use AI/ML

2025/7/4
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MLOps.community

AI Chapters Transcript
Chapters
This chapter explores Uber's AI platform, Michelangelo, and its role in powering various business-critical machine learning use cases. It discusses the platform's evolution and how it supports diverse developer needs and model types.
  • Uber uses a four-tier system to categorize its machine learning projects based on business impact.
  • Michelangelo powers 100% of Uber's business-critical ML use cases.
  • Michelangelo's evolution includes adapting to support deep learning and providing flexibility for developers to choose their tools.

Shownotes Transcript

Kai Wang joins the MLOps Community podcast LIVE to share how Uber built and scaled its ML platform, Michelangelo. From mission-critical models to tools for both beginners and experts, he walks us through Uber’s AI playbook—and teases plans to open-source parts of it.

// Bio

Kai Wang is the product lead of the AI platform team at Uber, overseeing Uber's internal end-to-end ML platform called Michelangelo that powers 100% Uber's business-critical ML use cases.

// Related Links

Uber GenAI: https://www.uber.com/blog/from-predictive-to-generative-ai/)

#uber #podcast #ai #machinelearning 




Catch all episodes, blogs, newsletters, and more: [https://go.mlops.community/TYExplore](https://go.mlops.community/TYExplore))

MLOps Swag/Merch: [[https://shop.mlops.community/](https://shop.mlops.community/))]

Connect with Demetrios on LinkedIn: [/dpbrinkm](https://www.linkedin.com/in/dpbrinkm/))

Connect with Kai on LinkedIn: [/kai-wang-67457318/](https://www.linkedin.com/in/kai-wang-67457318/))

Timestamps:



[00:00] Rethinking AI Beyond ChatGPT

[04:01] How Devs Pick Their Tools

[08:25] Measuring Dev Speed Smartly

[10:14] Predictive Models at Uber

[13:11] When ML Strategy Shifts

[15:56] Smarter Uber Eats with AI

[19:29] Summarizing Feedback with ML

[23:27] GenAI That Users Notice

[27:19] Inference at Scale: Michelangelo

[32:26] Building Uber’s AI Studio

[33:50] Faster AI Agents, Less Pain

[39:21] Evaluating Models at Uber

[42:22] Why Uber Open-Sourced Machanjo

[44:32] What Fuels Uber’s AI Team