Jai Ranganathan (KeepTruckin)
Jai is currently SVP Product at KeepTruckin, and was formerly VP of various AI and Data matters at Uber.
End-To-End Use Case of Uber's COTA system
  • A tool that uses machine learning and natural language processing techniques to help agents deliver better customer support.
  • Enables quick and efficient issue resolution for more than 90 percent of Uber's inbound support tickets.

Challenge

As Uber grows, so does the volume of support tickets
  • Millions of tickets from riders, drivers, and eaters per week
  • Global-scale of serving 600+ cities
  • Thousands of different types of issues users may encounter
  • Multilingual support
https://eng.uber.com/cota/

Customer Support Platform

  • Steps in the workflow
    • User → Select Flow Node → Write Message → Contact Ticket → Customer Support Representative → Select Contact Type → Lookup Info and Policies → Select Action → Write Response Using a Reply Template → Response → User
  • Problems to solve
    • Issue prediction
    • Issue categorization
    • Ticket routing
    • Ticket volume
    • Policy optimization
    • Auto-response

Exploration

  • Identify the right problems to solve
    • Use analytics to understand the value before all else
    • Know what metrics to optimize for
  • Understand whether Machine Learning is a good fit
  • Build with an eye on the probabilistic nature of Machine Learning solutions

Development

  • Many possible solutions including basic Machine Learning techniques
  • Understand the cost-benefit of compute time vs accuracy
  • Deep learning is a fast-evolving space - keep up with the literature to understand the latest advances
  • Validate your results with visualization

Deployment

  • Architecture complexity with feature engineering and training have special needs
  • Deep learning is still slow! Distributed deep learning can help a lot and is getting better
  • Good experiment design required to validate the models

Monitoring

  • Dynamic business problems require retraining strategies with well thought out safe deployment
  • Continuous improvement of labeling will make your models better
  • Look for edges where your models fail to find room for model improvements