Promoting Distributed Innovation // Melissa Barr & Michael Mui // MLOps Podcast #154 video clip
MLOps Coffee Sessions #154 with Melissa Barr & Michael Mui, Machine Learning Education at Uber co-hosted by Lina Weichbrodt.
Melissa and Michael discuss the importance of creating modules and courses to educate teams on how to use specific features, which can reduce friction and promote distributed innovation within a company.
They agree that such programs should exist, even if the complexity of the organization doesn't initially warrant it, as it can scale over time. The goal of their blog post https://www.uber.com/blog/ml-education-at-uber/ is to showcase different components that can be customized for different companies, as there is no one-size-fits-all solution.
// 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
Michael 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, Ber
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