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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: 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

Speaker: Chong Sun

Senior Software Engineer @Uber

Find Chong Sun at

Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches, including image recognition, NLP, predictions, & modeling.

  • Deep Learning in Practice

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe including use cases on machine translation, computer vision, & image recogition.

  • AI Meets the Physical World

    Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.

  • Papers to Production: CS in the Real World

    Groundbreaking papers make real world impact.

  • Solving Software Engineering Problems with Machine Learning

    Anomaly detection, ML in IDE's, bayesian optimization for config. Machine Learning techniques for more effective software engineering.

  • Predictive Architectures in the Real World

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.