62 Best Uber Machine Learning Blog Posts
Tags: machinelearning
Published: 2019-12-05 23:29:49

Here are 62 engineering blog posts on how Uber uses machine learning to route drivers and pick up millions of passengers:

8. [Meet Michelangelo: Uber’s Machine Learning Platform](https://eng.uber.com/michelangelo/)
Uber Engineering introduces Michelangelo, their machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.

2. [Scaling Machine Learning at Uber with Michelangelo](https://eng.uber.com/scaling-michelangelo/)
Uber built Michelangelo, their machine learning platform, in 2015. Three years later, they reflect their journey to scaling ML at Uber and lessons learned along the way.

1. [Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber](https://eng.uber.com/neural-networks-uncertainty-estimation/)
Uber Engineering introduces a new Bayesian neural network architecture that more accurately forecasts time series predictions and uncertainty estimations.


3. [Evolving Michelangelo Model Representation for Flexibility at Scale](https://eng.uber.com/michelangelo-model-representation/)
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.

4. [Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development](https://eng.uber.com/michelangelo-pyml/)
Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.

5. [Searchable Ground Truth: Querying Uncommon Scenarios in Self-Driving Car Development](https://eng.uber.com/searchable-ground-truth-atg/)
When developing Uber's self driving car systems, engineers found a way to identify edge case scenarios amongst terabytes of sensor data representing real-world situations.

6. [Science at Uber: Improving Transportation with Artificial Intelligence](https://eng.uber.com/improving-transportation-artificial-intelligence/)
Uber Chief Scientist Zoubin Ghahramani explains how artificial intelligence went from academia to real-world applications, and how Uber uses it to make transportation better.

7. [Three Approaches to Scaling Machine Learning with Uber Seattle Engineering](https://eng.uber.com/three-approaches-to-scaling-machine-learning-with-uber-seattle-engineering/)
At an April 2019 meetup on ML and AI at Uber Seattle, members of their engineering team discussed three different approaches to enhancing their ML ecosystem.

8. [Science at Uber: Powering Machine Learning at Uber](https://eng.uber.com/uber-science-machine-learning-platform/)
Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.

8. [Introducing LCA: Loss Change Allocation for Neural Network Training](https://eng.uber.com/loss-change-allocation/)
Uber AI Labs proposes Loss Change Allocation (LCA), a new method that provides a rich window into the neural network training process.

8. [Using GraphQL to Improve Data Hydration in their Customer Care Platform and Beyond](https://eng.uber.com/graphql-data-hydration-customer-care/)
Uber Engineering details how GraphQL integrated into their Customer Care platform, making for more targeted queries and reducing server load.

8. [Science at Uber: Making a Real-world Impact with Data Science](https://eng.uber.com/making-a-real-world-impact-with-data-science/)
Suzette Puente, Uber Data Science Manager, shares how she applies her graduate work in statistics to forecast traffic patterns and generate better routes.

8. [Science at Uber: Applying Artificial Intelligence at Uber](https://eng.uber.com/applying-artificial-intelligence-at-uber/)
Zoubin Ghahramani, Head of Uber AI, discusses how they use artificial intelligence techniques to make their platform more efficient for users.

8. [Science at Uber: Powering Uber’s Ridesharing Technologies Through Mapping](https://eng.uber.com/science-at-uber-powering-ubers-ridesharing-technologies-through-mapping/)
Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.

8. [Science at Uber: Bringing Research to the Roads](https://eng.uber.com/bringing-research-to-the-roads/)
Uber Principal Engineer Waleed Kadous discusses how they assess technologies their teams can leverage to improve the reliability and performance of their platform.

8. [Science at Uber: Building a Data Science Platform at Uber](https://eng.uber.com/building-ubers-data-science-platforms/)
Uber Director of Data Science Franziska Bell discusses how they created data science platforms at Uber, letting employees of all technical skills perform forecasts and analyze data.

8. [Making Apache Spark Effortless for All of Uber](https://eng.uber.com/uscs-apache-spark/)
Uber engineers created uSCS, a Spark-as-a-Service solution that helps manage Apache Spark jobs throughout large organizations.

8. [Gaining Insights in a Simulated Marketplace with Machine Learning at Uber](https://eng.uber.com/simulated-marketplace/)
Uber's Marketplace simulation platform leverages ML to rapidly prototype and test new product features and hypotheses in a risk-free environment.

8. [No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox](https://eng.uber.com/ludwig-deep-learning-toolbox/)
Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.

8. [Using Causal Inference to Improve the Uber User Experience](https://eng.uber.com/causal-inference-at-uber/)
Uber Labs leverages causal inference, a statistical method for better understanding the cause of experiment results, to improve their products and operations analysis.

8. [Power On: Accelerating Uber’s Self-Driving Vehicle Development with Data](https://eng.uber.com/accelerating-self-driving-vehicle-development-with-data/)
A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In their development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe.

8. [Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask](https://eng.uber.com/deconstructing-lottery-tickets/)
Uber builds upon the Lottery Ticket Hypothesis by proposing explanations behind these mechanisms and deriving a surprising by-product: the Supermask.

8. [Improving Uber’s Mapping Accuracy with CatchME](https://eng.uber.com/mapping-accuracy-with-catchme/)
CatchMapError (CatchMe) is a system that automatically catches errors in Uber's map data with anonymized GPS traces from the driver app.

8. [Solving Big Data Challenges with Data Science at Uber](https://eng.uber.com/solving-big-data-challenges-with-data-science-at-uber/)
How engineers and data scientists at Uber came together to come up with a means of partially replicating Vertica clusters to better scale their data volume.

8. [Accessible Machine Learning through Data Workflow Management](https://eng.uber.com/machine-learning-data-workflow-management/)
Uber engineers offer two common use cases showing how they orchestrate machine learning model training in their data workflow engine.

8. [Using Machine Learning to Ensure the Capacity Safety of Individual Microservices](https://eng.uber.com/machine-learning-capacity-safety/)
Uber leveraged machine learning to design their capacity safety forecasting tooling with a special emphasis on calculating a quality of reliability score.

8. [Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training](https://eng.uber.com/horovod-pyspark-apache-mxnet-support/)
Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.

8. [Modeling Censored Time-to-Event Data Using Pyro, an Open Source Probabilistic Programming Language](https://eng.uber.com/modeling-censored-time-to-event-data-using-pyro/)
Censored time-to-event data is critical to the proper modeling and understanding of customer engagement on the Uber platform. In this article, they demonstrate an easier way to model this data using Pyro.

8. [Introducing Ludwig, a Code-Free Deep Learning Toolbox](https://eng.uber.com/introducing-ludwig/)
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.

8. [Why Financial Planning is Exciting… At Least for a Data Scientist](https://eng.uber.com/financial-planning-for-data-scientist/)
In this article, Uber’s Marianne Borzic Ducournau discusses why financial planning at Uber presents unique and challenging opportunities for data scientists.

8. [How Uber Leverages Applied Behavioral Science at Scale](https://eng.uber.com/applied-behavioral-science-at-scale/)
Uber Labs utilizes insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for users on their platform.

8. [Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber](https://eng.uber.com/manifold/)
Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.

8. [POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer](https://eng.uber.com/poet-open-ended-deep-learning/)
Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.

8. [Horovod Joins the LF Deep Learning Foundation as its Newest Project](https://eng.uber.com/horovod-deep-learning-foundation/)
Horovod, Uber's distributed training framework, joins the LF Deep Learning Foundation to help advance open source innovation in AI, ML, and deep learning.

8. [Faster Neural Networks Straight from JPEG](https://eng.uber.com/neural-networks-jpeg/)
Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations.

8. [NVIDIA: Accelerating Deep Learning with Uber’s Horovod](https://eng.uber.com/nvidia-horovod-deep-learning/)
Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.

8. [Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps](https://eng.uber.com/nlp-deep-learning-uber-maps/)
To improve their maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.

8. [Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning](https://eng.uber.com/petastorm/)
Uber's Advanced Technologies Group introduces Petastorm, an open source data access library enabling training and evaluation of deep learning models directly from multi-terabyte datasets in Apache Parquet format.

8. [Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning](https://eng.uber.com/cota-v2/)
Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.

8. [Forecasting at Uber: An Introduction](https://eng.uber.com/forecasting-introduction/)
In this article, they provide a general overview of how their teams leverage forecasting to build better products and maintain the health of the Uber marketplace.

8. [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://eng.uber.com/coordconv/)
As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.

8. [Transforming Financial Forecasting with Data Science and Machine Learning at Uber](https://eng.uber.com/transforming-financial-forecasting-machine-learning/)
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.

8. [How Trip Inferences and Machine Learning Optimize Delivery Times on Uber Eats](https://eng.uber.com/uber-eats-trip-optimization/)
Using GPS and sensor data from Android phones, Uber engineers develop a state model for trips taken by Uber Eats delivery-partners, helping to optimize trip timing for delivery-partners and eaters alike.

8. [Advanced Technologies for Detecting and Preventing Fraud at Uber](https://eng.uber.com/advanced-technologies-detecting-preventing-fraud-uber/)
To detect and prevent fraud, Uber brings to bear data science and machine learning, analyzing GPS traces and usage patterns to identify suspicious behavior.

8. [Food Discovery with Uber Eats: Building a Query Understanding Engine](https://eng.uber.com/uber-eats-query-understanding/)
Uber engineers share how they process search terms for their Uber Eats service, using query understanding and expansion to find restaurants and menu items that best match what their eaters want.

8. [From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox](https://eng.uber.com/keplergl/)
Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.

8. [Engineering a Job-based Forecasting Workflow for Observability Anomaly Detection](https://eng.uber.com/observability-anomaly-detection/)
Uber’s Observability Applications team overhauled their anomaly detection platform’s workflow to enable the intuitive and performant backfilling of forecasts, paving the way for more intelligent alerting.

8. [Differentiable Plasticity: A New Method for Learning to Learn](https://eng.uber.com/differentiable-plasticity/)
Artificial intelligence researchers develop new method to let neural networks continue to learn, even after initial training.

8. [COTA: Improving Uber Customer Care with NLP & Machine Learning](https://eng.uber.com/cota/)
In this article, Uber Engineering introduces their Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.

8. [Welcoming the Era of Deep Neuroevolution](https://eng.uber.com/deep-neuroevolution/)
By leveraging neuroevolution to train deep neural networks, Uber AI Labs is developing solutions to solve reinforcement learning problems.

8. [Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning](https://eng.uber.com/activity-matrix/)
Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on their driver app.

8. [Engineering More Reliable Transportation with Machine Learning and AI at Uber](https://eng.uber.com/machine-learning/)
In this article, they highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.

8. [Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language](https://eng.uber.com/pyro/)
Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.

8. [Turbocharging Analytics at Uber with their Data Science Workbench](https://eng.uber.com/dsw/)
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.

8. [Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow](https://eng.uber.com/horovod/)
Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.

8. [Engineering Uber Predictions in Real Time with ELK](https://eng.uber.com/elk/)
Uber Engineering architected a real-time trip features prediction system using an open source RESTful search engine built with Elasticsearch, Logstash, and Kibana (ELK).

8. [Detecting Abuse at Scale: Locality Sensitive Hashing at Uber Engineering](https://eng.uber.com/lsh/)
In this article, they discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.

8. [Building an Intelligent Experimentation Platform with Uber Engineering](https://eng.uber.com/experimentation-platform/)
Composed of a staged rollout and intelligent analytics tool, Uber Engineering's experimentation platform is capable of stably deploying new features at scale across their apps. In this article, they discuss the challenges and opportunities they faced when building this product.

8. [Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop](https://eng.uber.com/hoodie/)
Uber Engineering's data processing platform team recently built and open sourced Hudi, an incremental processing framework that supports their business critical data pipelines. In this article, they see how Hudi powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.

8. [Engineering Uber Systems to Combat Fraud](https://eng.uber.com/fraud-prevention-team-profile/)
Fraud prevention is one of Uber's fastest growing areas of research and development. As their platform has grown, so has the international underworld that tries to undermine it. Here’s how Uber engineers systems to fight fraud in 2016 and beyond.

8. [How Uber Engineering Increases Safe Driving with Telematics](https://eng.uber.com/telematics/)
The engineering behind how Uber's Driving Safety team is using telematics to raise awareness of driving patterns to their partners.

8. [Engineer Q&A: Doing Data Science at Uber Engineering](https://eng.uber.com/emi-data-science-q-a/)
This week, Emi Wang dishes out data knowledge on what she’s been up to at Uber since she joined in September 2012.

*This post first appeared on the FullJoin blog at https://fulljoin.io/blog/uber-machine-learning*