Uber的ML教育：受工程原则启发的框架

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. 

在Uber，每秒钟都有数以百万计的机器学习（ML）预测，每天有数百名应用科学家、工程师、产品经理和研究人员从事ML解决方案的工作。

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.

Uber通过扩大机器学习的规模而获胜。我们在全社会范围内认识到，扩大机器学习应用的一个强大方式是通过教育。这就是为什么我们创建了机器学习教育计划：一个由工程原则驱动的计划，为向Uber技术员工提供Uber特有的ML教育资源提供了一个框架。

Figure 1: Uber’s ML Education program at a glance.

图1：Uber的ML教育计划一目了然。

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.

像一个生产系统一样，教育资源、内容和分配渠道需要不断地测量、评估和改进。确保ML教育计划的每一个组成部分都是在这个前提下设计的，这使我们能够迅速提供适合不同背景的工程师和科学家的新课程和课程。

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.

这篇文章分两部分，将重点介绍我们在设计和扩展这个项目时是如何应用工程原则的，以及它是如何帮助我们实现预期结果的。第一部分将介绍我们的设计原则，并解释将这些原则应用于技术教育内容设计和项目框架的好处，特别是在ML领域。第二部分将仔细研究该计划的关键组成部分，并思考使Uber的ML教育取得成功的结果。

Engineering Principles and ML Education: A Perfect Pair

工程原理和ML教育。一对完美的组合

At Uber, we recognize that not only is knowledge-sharing in technical subject matter area...