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

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

Author: Demetrios July 4, 2025 Duration: 45:23

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


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Connect with Kai on LinkedIn: /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


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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