Uploaded May 2018 | Updated September 2026, 2 weeks ago
In this presentation, Uber Sofia software engineer Stoyan Rachev discusses how we use dependency injections to make our code more modular and flexible and introduces our Fx Framework, a powerful solution that enables us to decouple object dependencies in Go.
In this presentation, Uber Sofia software engineer Stoyan Rachev discusses how we use dependency injections to make our code more modular and flexible and introduces our Fx Framework, a powerful solution that enables us to decouple object dependencies in Go.
![[Uber Seattle] Building Reliable Microservices @ Uber Siva Kalva](https://i.ytimg.com/vi/LFyK-Li2-ZQ/mqdefault.jpg "[Uber Seattle] Building Reliable Microservices @ Uber Siva Kalva
During a June 2018 meetup, engineering manager Siva Kalva discusses how we designed both the reliability tool chain our developers use when building microservices as well as their flow when deploying changes to production.")
![[Uber Open Source] New Features in Pinot: Standard SQL and Cloud Readiness](https://i.ytimg.com/vi/LayrViOqiOM/mqdefault.jpg "[Uber Open Source] New Features in Pinot: Standard SQL and Cloud Readiness
Apache Pinot is a distributed columnar database for real-time analytics. It powers many well-known applications, including LinkedIn’s Who Viewed My Profile feature and Uber Eats timely analytics. Since Pinot joined Apache Incubator in 2019, it has gained a lot of momentum with a growing community.
During this May 2020 Uber Engineering Meetup, software engineers Sidd and Xiang introduces new Apache Pinot integrations and features, including ORDER BY, DISTINCT, Text Search, and Standard SQL. They also discuss work being done to get Pinot cloud-ready, as well as its roadmap to Apache Incubator graduation.")
![[StackUp] TimescaleDB: Performant Time-series Data Management and Analytics with PostgresSQL](https://i.ytimg.com/vi/M59dAS_zjjg/mqdefault.jpg "[StackUp] TimescaleDB: Performant Time-series Data Management and Analytics with PostgresSQL
During this July 2019 meetup at Uber’s New York City office, Matvey Arye of Timescale discusses the advantages of building TimescaleDB as an extension of open source PostgreSQL.")
![[Payments Platform] To the Nines: Building Ubers Payments Processing System Paul Sorenson](https://i.ytimg.com/vi/MJABqwzBkHs/mqdefault.jpg "[Payments Platform] To the Nines: Building Ubers Payments Processing System Paul Sorenson
During a September 2018 meetup, software engineer Paul Sorenson offers a peek into Uber’s payment processing systems and the challenges of ensuring payment accuracy.")
![[Uber Seattle] Uber Bus Marketplace Tech Deep Dive](https://i.ytimg.com/vi/MvWXSK-Rslg/mqdefault.jpg "[Uber Seattle] Uber Bus Marketplace Tech Deep Dive
During an April 2019 meetup hosted at Uber’s Seattle office, Uber engineering managers Danny Guo and Eoin OMahony discuss designing algorithms to improve the route generation, fleet positioning and movement, dynamic routes, and walking optimization solutions behind Uber Bus.")
![[WiSDOM] Learning Values and Policies from Observation](https://i.ytimg.com/vi/NZFnIu4U4Aw/mqdefault.jpg "[WiSDOM] Learning Values and Policies from Observation
On October 24, 2019, the WiSDOM (Women in Statistics, Data, Optimization, and Machine Learning) Employee Resource Group hosted the second annual Moving The World With Data meetup, welcoming hundreds of members of the Bay Area tech community to learn about some of our toughest, most interesting data science problems.
In this presentation, Ashley Edwards, a research scientist with Uber AI Labs, shares original work from the machine learning discipline of reinforcement learning. Using examples from the game Coin Run and other simple tasks, she demonstrates how an agent can be made to learn latent policies through observation. She also posed a question: in the future, can we expect robots to learn to act like humans by watching us move?
Learn more about the meetup: https://eng.uber.com/moving-the-world-with-data")
![[Uber Open Source] Ludwig: A Code-free Deep Learning Toolbox](https://i.ytimg.com/vi/Ns_6Ep7GAIM/mqdefault.jpg "[Uber Open Source] Ludwig: A Code-free Deep Learning Toolbox
During this April 2019 meetup in San Francisco, Uber research scientist, Piero Molino introduces Ludwig, a deep learning toolbox that lets people without a machine learning background train prediction models without the need to write code. Ludwig is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike. By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.")
![[Visualization Nights] Designing Informational Analytics - JD Vogt & Raymon Sutedjo-The](https://i.ytimg.com/vi/Ntj5Cig68jE/mqdefault.jpg "[Visualization Nights] Designing Informational Analytics - JD Vogt & Raymon Sutedjo-The
Creating analytics apps and dashboards for business is more than picking the right chart. Its about understanding a users purpose and goals. During our June 2018 Visualization Night, JD Vogt and Raymon Sutedjo-The discuss how the Salesforce Analytics Design team approaches their work and the choices they make when creating analytics experiences, ranging from mapping out users path of inquiry to choosing the right charts and colors.")
