Presentation: Machine Learning Pipeline for Real-time Forecasting @Uber Marketplace

Track: Predictive Data Pipelines & Architectures

Location: Cyril Magnin I

Duration: 1:10pm - 2:00pm

Day of week: Tuesday

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Abstract

Uber's Marketplace is the algorithmic brain behind Uber's ride-sharing services. To help Marketplace systems make proactive and efficient decisions, the Marketplace Forecasting team builds and operates multiple machine learning models to produce forecast of many metrics, including supply and demand, over both granular time and a large number of geo-spatial dimensions.

To empower both data scientists and engineers to build and manage models that range from regressions to neural networks in production, the Marketplace Forecasting team has built an highly scalable and automated machine learning platform that supports efficient feature engineering, distributed model training, turn-key model deployment, metric-based automatic model selection, and scalable model serving.

This talk will discuss how deep learning helps improve the accuracy and efficiency of our forecasting models, the architecture of the machine learning platform, how it was evolved from a simple ad-hoc system, and lessons learned in running the platform in production.

Speaker: Chong Sun

Senior Software Engineer @Uber

Find Chong Sun at

Speaker: Danny Yuan

Real-time Streaming Lead @Uber

Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. Prior to joining Uber, he worked on building Netflix’s cloud platform. His work includes predictive autoscaling, distributed tracing service, real-time data pipeline that scaled to process hundreds of billions of events every day, and Netflix’s low-latency crypto services.

Find Danny Yuan at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.