Year in Review: 2017 Highlights from the Uber Engineering Blog

This year, the Uber Engineering Blog has shared several of the ways in which our technologies have improved user experiences across our services. From (most recently) welcoming the era of deep neuroevolution and unifying mobile onboarding experiences, to open sourcing a streaming analytics platform and launching new features that celebrate drivers, our team has been busy!

As we gear up for the New Year, let us revisit some of our editor’s picks from 2017:  

Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.

Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.

Uber Engineering’s Data Visualization Team and Advanced Technologies Group built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.

Recurrent neural networks equip Uber Engineering’s new forecasting model to more accurately predict rider demand during extreme events.

In this article, we discuss how Uber Engineering designed m.uber, a lightweight web app that delivers a native app experience for riders on mobile browsers.

If you are interested in learning more about Uber Engineering’s ever-evolving tech stack, follow us on Twitter or subscribe to our newsletter.

Photo Header Credit, “Giraffe silhouette crossing at sunset,” by Conor Myhrvold, Okavango Delta, Botswana, 2005.