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
Last modified 1yr ago