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Engineering Production NLP Systems at T-Mobile with Heather Nolis - #600

2022-11-21 · 44 min · episode 600 · 14 entities

episode page audio

Asserted relationships

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    Engineering Production NLP Systems at T-Mobile with Heather Nolis - #600
  • → hosted by Sam Charrington person
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    Feed author/publisher: Sam Charrington
  • → discusses Science concept
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  • → discusses Technology company
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  • → discusses Tech News concept
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    Feed category: Tech News
  • → hosted by TWIML company
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    Feed author/publisher: TWIML

Entities found in this episode

concepts 6

  • mentioned Science concept
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    Feed category: Science
  • mentioned Tech News concept
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    evidence rules-v4
    Feed category: Tech News
  • discusses Science concept
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    Feed category: Science
  • discusses News concept
    0.40
    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
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    Feed category: Tech News
  • mentioned NLP concept
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    NLP

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Technology company
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    Feed category: Technology
  • discusses Technology company
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    Feed category: Technology
  • hosted by TWIML company
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    evidence rules-v4
    Feed author/publisher: TWIML

persons 3

  • mentioned Heather Nolis person
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    Engineering Production NLP Systems at T-Mobile with Heather Nolis - #600
  • mentioned Sam Charrington person
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    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
Today we’re joined by Heather Nolis, a principal machine learning engineer at T-Mobile. In our conversation with Heather, we explored her machine learning journey at T-Mobile, including their initial proof of concept project, which held the goal of putting their first real-time deep learning model into production. We discuss the use case, which aimed to build a model customer intent model that would pull relevant information about a customer during conversations with customer support. This process has now become widely known as blank assist. We also discuss the decision to use supervised learning to solve this problem and the challenges they faced when developing a taxonomy. Finally, we explore the idea of using small models vs uber-large models, the hardware being used to stand up their infrastructure, and how Heather thinks about the age-old question of build vs buy.