MLOps Coffee Sessions #156 with Melissa Barr & Michael Mui, Machine Learning Education at Uber co-hosted by Lina Weichbrodt.

// Abstract

Melissa and Michael discuss the education program they developed for Uber's machine learning platform service, Michelangelo, during a guest appearance on a podcast. The program teaches employees how to use machine learning both in general and specifically for Uber. The platform team can obtain valuable feedback from users and use it to enhance the platform. The course was designed using engineering principles, making it applicable to other products as well.

// Bio

Melissa Barr

Melissa is a Technical Program Manager for ML & AI at Uber. She is based in New York City. She drives projects across Uber’s ML platform, delivery, and personalization teams. She also built out the first version of the ML Education Program in 2021.

Michael Mui

Melissa is a Staff Technical Lead Manager on Uber AI's Machine Learning Platform team. He leads the Distributed ML Training team which focuses on building elastic, scalable, and fault-tolerant distributed machine learning libraries and systems used to power machine learning development productivity across Uber. He also co-leads Uber’s internal ML Education initiatives. Outside of Uber, Michael also teaches ML at the Parsons School of Design in NYC as an Adjunct Faculty (mostly for the museum passes!) and guest lectures at the University of California, Berkeley.

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// Related Links

https://www.uber.com/blog/ml-education-at-uber-program-design-and-outcomes/https://www.uber.com/blog/ml-education-at-uber/https://www.uber.com/en-PH/blog/ml-education-at-uber/

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Timestamps:

[00:00] Melissa and Michael's preferred coffee

[01:51] Takeaways

[05:40] Please subscribe to our newsletters and leave reviews on our podcasts!

[06:18] Machine learning at Uber education program

[07:45] The Uber courses

[10:03] Tailoring the Uber education system

[12:27] Growing out of the ML-Ed platform efforts

[14:14] Expanding the ML Market Size

[15:23] Relationship evolution

[17:36] Reproducibility best practices

[21:46] Learning development timeline

[26:29] Courses effectiveness evaluation

[29:57] Tracking Progress Challenge

[31:25] ML platforms for internal tools

[35:07] Impact of ML Education at Uber

[39:30] Recommendations to companies who want to start an ML-Ed platform

[41:12] Early ML Adoption Program

[42:11] Homegrown or home-built platform

[42:54] Feature creation to a course

[45:24] ML Education at Uber: Frameworks Inspired by Engineering Principles

[49:42] The Future of ML Education at Uber

[52:28] Unclear ways to spread ML knowledge

[54:20] Module for Generative AI and ChatGPT

[55:05] Measurement of success

[56:39] Wrap up