Presentation: Uber's Spatio-Temporal Data
Abstract
Uber’s Marketplace is the algorithmic brains and decision engine behind our ride-sharing services. Marketplace Forecasting builds and deploys spatio-temporal models and forecasts to enable hyperlocal decision making. To model the physical world requires us to reimagine how we look at the basic problem of forecasting.
We will discuss the challenges of modeling the influence of external signals, such as global news events and holidays in our Marketplace. In the majority of cases, there is limited historical data, and in cases where cities have just launched, there is no data at all. We will briefly cover how different techniques ranging from linear to deep learning models, generalized embeddings and cutting edge AI to help us forecast the future states of the Marketplace and even predict the onset of extreme events before they occur!
2019 Tracks
-
Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
-
Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
-
Predictive Data Pipelines & Architectures
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
-
Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
-
ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
-
Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.
-
Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
-
Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
-
Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.
-
Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.