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.
Chris Hausler leads the data science team at Zendesk, a role he describes as turning lots of data into magic, which he does with the help of machine learning, Python, Hadoop, graphs galore, and amazing colleagues. Over his career, he’s held the titles of data scientist, data engineer, researcher, PhD student, consultant, and programmer.
Arwen Griffioen is a data scientist at Zendesk, where she works on the team producing deep learning solutions for customer self-service. An Oregonian expat who has lived in Melbourne for the past seven years, Arwen is passionate about improving the status of under represented groups in STEM fields and applying machine learning to make the world a little bit better. She holds a PhD in machine learning with a minor in ecoinformatics.
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