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Engineering, Culture, Data / ML, Uber AI

ML Education at Uber: Frameworks Inspired by Engineering Principles

July 28, 2022 / Global
Featured image for ML Education at Uber: Frameworks Inspired by Engineering Principles
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Figure 1: Uber’s ML Education program at a glance.
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Figure 2: Reproducibility and observability in Uber’s ML infrastructure.
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Figure 3: Uber’s ML Education program design principles – why they’re beneficial and how they’re implemented.
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Figure 4: ML Education’s course development workflow.
Brooke Carter

Brooke Carter

Brooke Carter is a Program Manager based in Seattle, WA. She leads end-to-end strategy and execution of Uber’s Machine Learning Education Program. She focuses on scaling the program’s impact globally and designing program experiments to drive efficiencies in ML Education’s operational frameworks.

Melissa Barr

Melissa Barr

Melissa Barr is a Senior Technical Program Manager on Uber’s AI Platform team. She is based in New York City. She drives a broad set of programs across ML & AI, specializing in topics with embeddings, recommendation systems, and large language models.

Michael Mui

Michael Mui

Michael Mui is a Staff Software Engineer on Uber AI's Machine Learning Platform team. He works on the distributed training infrastructure, hyperparameter optimization, model representation, and evaluation. He also co-leads Uber’s internal ML Education initiatives.

Posted by Brooke Carter, Melissa Barr, Michael Mui