Uber Engineering Blog

If you have read our previous article, ML Education at Uber: Frameworks Inspired by Engineering Principles, you have seen several examples of how Uber benefits from applying Engineering Principles to drive the ML Education Program’s content design and program frameworks. In this follow-up, we will dig deeper into what we believe to be other unique aspects of ML Education at Uber: our approach to Content Components, Content Delivery, Observability, and Marketing & Reach.

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.

“Immensely laborious calculations on inferior data may increase the yield from 95 to 100 percent. A gain of 5 percent, of perhaps a small total. A competent overhauling of the process of collection, or of the experimental design, may often increase the yield ten- or twelve-fold, for the same cost in time and labor. To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.

This blog post describes the implementation of an automated vertical CPU scaling system in which every storage workload running at Uber is allocated the ideal amount of cores. The framework is used today to right-size more than 500,000 Docker containers, and since its inception it has applied a net reduction of allocations of more than 120,000 cores, leading to annual multi-million dollar savings in infrastructure spending.

Uber is a data-driven company that heavily relies on offline and online analytics for decision-making. As Uber’s data grows exponentially every year, it’s crucial to process this data very efficiently and with minimum cost. Over the years, Apache Spark™ has become the primary compute engine at Uber to satisfy such data needs. Spark empowers many business-critical use cases at Uber with its unique features, including Uber rides, Uber Eats, autonomous vehicles, ETAs, Maps, and many more.

Shadower is a load testing tool that allows us to provide load testing as a service to any microservice at Uber. Shadower started as a command line application that allowed us to read a local file to load test a local application. At the time, Maps PEs were heavily investing in Java GC tuning. We needed Shadower to be able to do request mirroring to make sure two different applications get about the same load and different types of loads (test multiple endpoints).

Before 2021, Uber engineers would have to take quite a taxing journey to make a code change to the Go Monorepo. First, the engineer would make their changes on a local branch and put up a code revision to our internal code review system, Phabricator. Next, our infrastructure would see the request and initiate a number of validation jobs on our CI.

At Uber we use data from user support interactions to identify gaps in our products and create better, more delightful experiences for our users. Support interactions with customers include information about broken product experiences, any technical or operational issues faced, and even their general sentiment towards the product and company. Understanding the root cause of a broken product experience requires additional context, such as details of the trip or the order.

Justification for Identity Verification Latin America is a rich cultural region, known for its world-renowned gastronomy, its abundant biodiversity, and its welcoming population. However, socio-economic inequality has been a challenge for the region, and is generally considered a major contributing factor to high levels of violence. The platform is not immune to the environment in which it operates.

Uber has adopted Golang (Go for short) as a primary programming language for developing microservices. Our Go monorepo consists of about 50 million lines of code (and growing) and contains approximately 2,100 unique Go services (and growing). Go makes concurrency a first-class citizen; prefixing function calls with the go keyword runs the call asynchronously. These asynchronous function calls in Go are called goroutines.