Aha moments in deep learning at Zendesk

Description

Chris Hausler and Arwen Griffioen discuss Zendesk’s experience with deep learning, using the example of Answer Bot, a question-answering system that resolves support tickets without agent intervention. They cover the benefits Zendesk has already seen and challenges encountered along the way.

Answer Bot uses deep learning to understand customer queries, responding with relevant knowledge base articles that allow customers to self-serve. Research and development behind the ML models underpinning Answer Bot has been rewarding but punctuated with pivotal deviations from the charted course: deep learning was not Zendesk’s first approach. Chris and Arwen walk you through the journey from product ideation to traditional ML approaches with per-customer models to the current release that utilizes word embeddings and recurrent neural networks to provide a single global model that can serve tens of thousands of accounts.

Topics include:

• Defining the problem space and the metrics Zendesk wanted to optimize for
• How the team approached the problem with traditional ML
• Why they chose to take a big bet and pivot to deep learning
• Getting the team up to speed with deep learning and TensorFlow
• Why deep learning is great
• Why deep learning is great—but not magic
• Processes and frameworks for experimentation, iteration, and validation