Deduping And Storing Images At Uber Eats

- Kristoffer Andersen tl;dr: "The Uber Eats system handles several hundred million product images and millions of image updates are performed every hour. We have implemented a content-addressable caching layer that very effectively detects duplicates and thereby reduces download times, processing times, and storage costs."

featured in #374

A Few Thoughts About Uber's Breach

tl;dr: "Allegedly, an 18 year old spammed an employee with 2FA via push notifications on an employee with a known password. They got into the VPN and scanned for servers, found a file share without any access controls, and a script that could access break-the-glass credentials. With the highest level of credentials available, they then got effective root access to Slack, AWS, Google Suite, and active directory at Uber."

featured in #353

Supercharging A/B Testing At Uber

tl;dr: "While the statistical underpinnings of A/B testing are a century old, building a correct and reliable A/B testing platform and culture at a large scale is still a massive challenge... Uber went through a similar journey and this blog post describes why and how we rebuilt the A/B testing platform we had at Uber."

featured in #337

The Platform And Program Split At Uber: A Milestone Special

- Gergely Orosz tl;dr: "More than 100 people would need to be hired across engineering, product and design, to staff these teams. The new teams were stack ranked by importance e.g. teams responsible for growing the supply of drivers were ranked much higher than those generating rider demand." Gergely discusses Uber's biggest engineering organizational change: creating cross-functional program teams and introducing platform teams.

featured in #330

Uber’s Unified Signup and Login Stack

tl;dr: "Over the years we’ve built independent signup and login experiences for each of our lines of business which allowed us to innovate and move a lot quicker. However, as we scaled and added additional lines of business, our experiences began to diverge leading to some of these inconsistencies being amplified."

featured in #323

DeepETA: How Uber Predicts Arrival Times Using Deep Learning

tl;dr: The ML model takes into account spatial and temporal features, such as the origin, destination and time of the request, as well information about real-time traffic and the nature of the request, such as whether it is a delivery dropoff or rideshare pickup."

featured in #293