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