![[Uber Seattle] Building Reliable Microservices @ Uber: An Introduction Prabhu Krishnamoorthy @UberEngineering](https://i.ytimg.com/vi/c3RPjClWamY/maxresdefault.jpg)
Uploaded October 2018 | Updated September 2026, 2 weeks ago
During this June 2018 meetup presentation, Uber engineering manager Prabhu Krishnamoorthy discusses how we build the tooling that powers our microservices and ensure that our distributed architecture is reliable, highly available, and scalable.
During this June 2018 meetup presentation, Uber engineering manager Prabhu Krishnamoorthy discusses how we build the tooling that powers our microservices and ensure that our distributed architecture is reliable, highly available, and scalable.
![[Uber Seattle] Building Reliable Microservices @ Uber: An Introduction Prabhu Krishnamoorthy](https://i.ytimg.com/vi/c3RPjClWamY/mqdefault.jpg "[Uber Seattle] Building Reliable Microservices @ Uber: An Introduction Prabhu Krishnamoorthy
During this June 2018 meetup presentation, Uber engineering manager Prabhu Krishnamoorthy discusses how we build the tooling that powers our microservices and ensure that our distributed architecture is reliable, highly available, and scalable.")
![[MoneyCon 2019] Evolution of Revenue Optimization at Dropbox](https://i.ytimg.com/vi/cARulgmUtW8/mqdefault.jpg "[MoneyCon 2019] Evolution of Revenue Optimization at Dropbox
The domain of Payments, Finance, and more generally FinTech, is a fast-growing industry that reached record global investment of $111.8B in 2018. The underlying technology that powers this incredible growth is also evolving rapidly. Hosted by Uber, MoneyCon 2019 brought together engineers from leading tech companies to present on the latest topics in payments engineering.
In this talk during MoneyCon 2019, Dropboxs Kirill Sapchuk and Evgeny Skarbovsky discuss how revenue optimization (fighting involuntary churn) has evolved at Dropbox over the last few years from some simpler ideas and experiments around retries up to an ML-based approach that they are working on today.")
![[Uber Open Summit 2018] Building Your Own Metrics Pipeline with M3](https://i.ytimg.com/vi/cHx4DuqINTM/mqdefault.jpg "[Uber Open Summit 2018] Building Your Own Metrics Pipeline with M3
To facilitate the growth of Uber’s global operations, we need to be able to quickly store and access billions of metrics on our back-end systems. As part of our robust and scalable metrics infrastructure, we built M3, an open source metrics platform that has been in use at Uber for several years now. During this talk, engineer Nikunj Aggarwal walks through what a general metrics pipeline looks like and how M3 components can be used for the various stages of the pipeline without locking you in to a particular vendor. He also covers some of the unique challenges we face at Uber’s scale and how the learnings from those can be used by other companies.
Learn more about Uber Open Source: https://uber.github.io/")
![[Uber Marketplace] An Introduction Chintan Turakhia](https://i.ytimg.com/vi/cdNn44JYtNw/mqdefault.jpg "[Uber Marketplace] An Introduction Chintan Turakhia
In this presentation from a May 2018 meetup, engineering manager Chintan Turakhia discusses how the Uber Marketplace leverages machine learning to deliver improved rider, driver, and eater experiences.")
![[XGBoost Meetup] Introduction to the XGBoost Community Nan Zhu & Philip Cho](https://i.ytimg.com/vi/dsqwIRrdcTA/mqdefault.jpg "[XGBoost Meetup] Introduction to the XGBoost Community Nan Zhu & Philip Cho
During a January 2019 XGBoost meetup hosted at Ubers SF office, Uber software engineer Nan Zhu and Amazon software engineer Philip Cho discuss the history and development of XGBoost, an open source software library that provides a gradient boosting framework for C++, Java, Python, R, and Julia.")
![[Uber Open Source] Profile Spark Applications at Scale Bo Yang](https://i.ytimg.com/vi/fCTdbX-mG2I/mqdefault.jpg "[Uber Open Source] Profile Spark Applications at Scale Bo Yang
During a June 2018 Open Source Meetup at our Seattle office, senior software engineer Bo Yang discusses how Ubers open source JVM Profiler enables us to profile Spark applications at scale across our microservices.")
![[Uber Seattle] Horovod: Distributed Deep Learning on Spark](https://i.ytimg.com/vi/fZ9P1v1jtFM/mqdefault.jpg "[Uber Seattle] Horovod: Distributed Deep Learning on Spark
During this April 2019 meetup, Uber engineer Travis Addair introduces the concepts that make Horovod work, and walks through how to make use of Horovod on Spark to add distributed training to machine learning pipelines. Horovod is a distributed training framework for TensorFlow, PyTorch, Keras, and MXNet. Scaling to hundreds of GPUs, Horovod can reduce training time from hours to minutes with just a handful of lines added to existing single-GPU training processes.")
