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Uber’s Finance Computation Platform

For a company of our size and scale, robust, accurate, and compliant accounting and analytics are a necessity, ensuring accurate and granular visibility into our financials, across multiple lines of business. Most standard, off-the-shelf finance engineering solutions cannot support the … The post Uber’s Finance Computation Platform appeared first...

Pinot Real-Time Ingestion with Cloud Segment Storage

Introduction Apache Pinot is an open source data analytics engine (OLAP), which allows users to query data ingested from as recently as a few seconds ago to as old as a few years back. Pinot’s ability to ingest real-time data … The post Pinot Real-Time Ingestion with Cloud Segment Storage appeared first on Uber Engineering Blog.

Uber’s Fulfillment Platform: Ground-up Re-architecture to Accelerate Uber’s Go/Get Strategy

Introduction to Fulfillment at Uber Uber’s mission is to help our consumers effortlessly go anywhere and get anything in thousands of cities worldwide. At its core, we capture a consumer’s intent and fulfill it by matching it with the right … The post Uber’s Fulfillment Platform: Ground-up Re-architecture to Accelerate Uber’s Go/Get Strategy appeared...

Containerizing Apache Hadoop Infrastructure at Uber

Introduction As Uber’s business grew, we scaled our Apache Hadoop (referred to as ‘Hadoop’ in this article) deployment to 21000+ hosts in 5 years, to support the various analytical and machine learning use cases. We built a team with varied … The post Containerizing Apache Hadoop Infrastructure at Uber appeared first on Uber Engineering Blog.

‘Orders Near You’ and User-Facing Analytics on Real-Time Geospatial Data

Introduction By its nature, Uber’s business is highly real-time and contingent upon geospatial data. PBs of data are continuously being collected from our drivers, riders, restaurants, and eaters. Real-time analytics over this geospatial data could provide powerful insights. In this … The post ‘Orders Near You’ and User-Facing Analytics on Real-Time...

Analyzing Customer Issues to Improve User Experience

Introduction The primary goal for customer support is to ensure users’ issues are addressed and resolved in a timely and effective manner. The kind of issues users face and what they say in their support interactions provides a lot of … The post Analyzing Customer Issues to Improve User Experience appeared first on Uber Engineering Blog.

Customer Support Automation Platform at Uber

High Level Overview of the Problem Introduction If you’ve used any online/digital service, chances are that you are familiar with what a typical customer service experience entails: you send a message (usually email aliased) to the company’s support staff, fill … The post Customer Support Automation Platform at Uber appeared first on Uber Engineering...

Tuning Model Performance

Introduction Uber uses machine learning (ML) models to power critical business decisions. An ML model goes through many experiment iterations before making it to production. During the experimentation phase, data scientists or machine learning engineers explore adding features, tuning parameters, … The post Tuning Model Performance appeared first...

Elastic Distributed Training with XGBoost on Ray

Introduction Since we productionized distributed XGBoost on Apache Spark™ at Uber in 2017, XGBoost has powered a wide spectrum of machine learning (ML) use cases at Uber, spanning from optimizing marketplace dynamic pricing policies for Freight, improving times of … The post Elastic Distributed Training with XGBoost on Ray appeared first on Uber...

Continuous Integration and Deployment for Machine Learning Online Serving and Models

Introduction At Uber, we have witnessed a significant increase in machine learning adoption across various organizations and use-cases over the last few years. Our machine learning models are empowering a better customer experience, helping prevent safety incidents, and ensuring market … The post Continuous Integration and Deployment for Machine...

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