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....
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...
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...
Optimal Feature Discovery: Better, Leaner Machine Learning Models Through Information Theory
Introduction
Suppose you own a production ML model that already works reasonably well. You know that adding relevant and diverse sources of signal to your...
Freight Pricing with a Controlled Markov Decision Process
Intro
Uber Freight was launched in 2017 to revolutionize the business of matching shippers and carriers in the huge and inefficient freight trucking industry (around...
Elastic Deep Learning with Horovod on Ray
Introduction
In 2017, we introduced Horovod, an open source framework for scaling deep learning training across hundreds of GPUs in parallel. At the time, most...
Horovod v0.21: Optimizing Network Utilization with Local Gradient Aggregation and Grouped Allreduce
We originally open-sourced Horovod in 2017, and since then it has grown to become the standard solution in industry for scaling deep learning training...
