ML Education at Uber: Frameworks Inspired by Engineering Principles

ML Education at Uber: Frameworks Inspired by Engineering Principles

摘要

At Uber, millions of machine learning (ML) predictions are made every second, and hundreds of applied scientists, engineers, product managers, and researchers work on ML solutions daily.

Uber wins by scaling machine learning. We recognize org-wide that a powerful way to scale machine learning adoption is by educating. That’s why we created the Machine Learning Education Program: a program driven by Engineering Principles that provides a framework for delivering Uber-specific ML educational resources to Uber Tech employees.

Like a production system, education resources, contents, and distribution channels need to be continuously measured, evaluated, and improved. Ensuring each component of the ML Education Program is designed on this premise enabled us to quickly deliver new courses and curriculum that are tailored to engineers and scientists of various backgrounds.

This 2-part article will focus on how we have applied engineering principles when designing and scaling this program, and how it has helped us achieve the desired outcome. Part 1 will introduce our design principles and explain the benefits of applying these principles to technical education content design and program frameworks, specifically in the ML domain. Part 2 will take a closer look at critical components of the program and reflect on the outcomes that make ML Education at Uber a success.