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

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

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.

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ML Use Cases at Think Big Analytics with Mo Patel and Laura Frølich - TWiML Talk #54

ML Use Cases at Think Big Analytics with Mo Patel and Laura Frølich - TWiML Talk #54

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. This time around, I speak with Mo Patel, practice director of AI & deep ...

6 Loka 201745min

Intel Nervana Devcloud with Naveen Rao & Scott Apeland - TWiML Talk #51

Intel Nervana Devcloud with Naveen Rao & Scott Apeland - TWiML Talk #51

In this episode, I talk to Naveen Rao, VP and GM of Intel’s AI Products Group, and Scott Apeland, director of Intel’s Developer Network. It's been a few months since we last spoke to Naveen, so he giv...

6 Loka 201737min

Ray: A Distributed Computing Platform for Reinforcement Learning with Ion Stoica - TWiML Talk #55

Ray: A Distributed Computing Platform for Reinforcement Learning with Ion Stoica - TWiML Talk #55

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. In this episode, I talk with Ion Stoica, professor of computer science &...

5 Loka 201728min

Topological Data Analysis with Gunnar Carlsson - TWiML Talk #53

Topological Data Analysis with Gunnar Carlsson - TWiML Talk #53

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. My guest for this show is Gunnar Carlsson, professor emeritus of mathema...

3 Loka 201733min

Bayesian Optimization for Hyperparameter Tuning with Scott Clark - TWiML Talk #50

Bayesian Optimization for Hyperparameter Tuning with Scott Clark - TWiML Talk #50

As you all know, a few weeks ago, I spent some time in SF at the Artificial Intelligence Conference. While I was there, I had just enough time to sneak away and catch up with Scott Clark, Co-Founder a...

2 Loka 201747min

Symbolic and Sub-Symbolic Natural Language Processing with Jonathan Mugan - TWiML Talk #49

Symbolic and Sub-Symbolic Natural Language Processing with Jonathan Mugan - TWiML Talk #49

Like last week’s interview with Bruno Goncalves, this week’s interview was also recorded at the last O’Reilly AI Conference back in New York in June. Also like last week’s show, this week’s is also fo...

25 Syys 201743min

Word2Vec & Friends with Bruno Gonçalves - TWiML Talk #48

Word2Vec & Friends with Bruno Gonçalves - TWiML Talk #48

This week i'm bringing you an interview from Bruno Goncalves, a Moore-Sloan Data Science Fellow at NYU. As you’ll hear in the interview, Bruno is a longtime listener of the podcast. We were able to co...

19 Syys 201732min

Evolutionary Algorithms in Machine Learning with Risto Miikkulainen - TWiML Talk #47

Evolutionary Algorithms in Machine Learning with Risto Miikkulainen - TWiML Talk #47

My guest this week is Risto Miikkulainen, professor of computer science at UT-Austin and vice president of Research at Sentient Technologies. Risto came locked and loaded to discuss a topic that we've...

11 Syys 201758min

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