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Uber

Past Presentations

Containers at Web Scale Panel

Containers and container management platforms are becoming a standard part of many infrastructures. At Facebook, Google, Netflix, and Uber container technologies have been a large part of their infrastructures for many years. By leveraging these technologies, each company has seen improvements in...

Kenny Yu Software Engineer @Facebook
Victor Marmol Staff Software Engineer @Google
Andrew Leung Senior Software Engineer @Netflix
Xiaojian Huang Engineer Manager @Uber
Wendy Dembowski Staff Software Engineer @Google
Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes

With the increasing scale of Uber’s business, efficient use of cluster resources is important to reduce the cost per trip. As we have learned when operating Mesos clusters in production, it is a challenge to overcommit resources for latency-sensitive services due to their large spread of...

Mayank Bansal Staff Engineer @Uber
Apoorva Jindal Senior Software Engineer @Uber
Time Predictions in Uber Eats

Uber Eats has been one of the fastest-growing meal delivery services since its initial launch in Toronto in December 2015. Currently, it’s available in over 40 countries and 400 cities. The ability to accurately predict delivery times is paramount to customer satisfaction and retention....

Zi Wang Leading the Machine Learning Engineering Work for Time Predictions @UberEats
Conquering Microservices Complexity @Uber With Distributed Tracing

Microservices bestow many benefits on the organizations adopting them, but they come with a steep price: complexity of the resulting architecture. Distributed tracing is a recognized way of dealing with that complexity and getting back the visibility into our systems. At Uber we discovered that...

Yuri Shkuro Creator of Jaeger & Software Engineer @Uber

Interviews

Zi Wang Leading the Machine Learning Engineering Work for Time Predictions @UberEats

Time Predictions in Uber Eats

What is that focus of your work today?

I’m currently leading the time prediction area for UberEats. As you can imagine, precise predictions are keys for the system’s efficiency and reliability. The features I’m working on are like predicting how long it will take to deliver the food to the eater, how long will the restaurant spend to prepare the food, etc....

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Yuri Shkuro Creator of Jaeger & Software Engineer @Uber

Conquering Microservices Complexity @Uber With Distributed Tracing

What is the focus of your work today?

I mostly work on distributed tracing, in the larger scope of overall observability.

Read Full Interview

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