Charlene Chambliss's guest appearances

Charlene Chambliss

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

The MLOps Podcast

Meredith Broussard

Ilias Diakonikolas

Scott Clark

Celeste Kidd

Dennis Mortensen

Rob Walker

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

Jason Holmberg

Lotte Bransen

Christopher Berner

David Spiegelhalter

OwlTail

In this episode, I'm speaking with Charlene Chambliss, Software Engineer at Aquarium. Charlene has vast experience getting NLP models to production. We dive into the intricacies of these models and how they differ from other ML subfields, the challenges in productionizing them, and how to get excited about data quality issues.   Join our Discord community: https://discord.gg/tEYvqxwhah Relevant Links:   ➡️Charlene on LinkedIn – https://www.linkedin.com/in/charlenechambliss/ ➡️Charlene on Twitter – https://twitter.com/blissfulchar   Recommendations:   🎬3blue1brown – Awesome YouTube channel about math &amp; science: https://www.youtube.com/c/3blue1brown  🎙NLP Highlights – Allen AI Insititute podcast about NLP research: https://soundcloud.com/nlp-highlights  🎙Software engineering daily: https://softwareengineeringdaily.com/  🎙TWiML – Another great podcast about machine learning and AI: https://twimlai.com/  📰Sebastian Ruder's blog and newsletter about NLP and ML: https://ruder.io/  📰Taming the Tail: Adventures in Improving AI Economics: https://a16z.com/2020/08/12/taming-the-tail-adventures-in-improving-ai-economics/  📰State of AI report (2021): https://www.stateof.ai/  📕Learn to learn – Ultralearning by Scott Young: https://www.scotthyoung.com/    🌐Check Out Our Website! https://dagshub.com Social Links:   🟦LinkedIn: https://www.linkedin.com/company/dagshub 🐦Twitter: https://twitter.com/TheRealDAGsHub 🐦Dean Pleban: https://twitter.com/DeanPlbn 

In this episode, I'm speaking with Charlene Chambliss, Software Engineer at Aquarium. Charlene has vast experience getting NLP models to production. We dive into the intricacies of these models and how they differ from other ML subfields, the challenges in productionizing them, and how to get excited about data quality issues.   Join our Discord community: <a href="https://discord.gg/tEYvqxwhah" rel="nofollow" target="_blank">https://discord.gg/tEYvqxwhah</a> Relevant Links:  <ul> <li>➡️Charlene on LinkedIn – <a href="https://www.linkedin.com/in/charlenechambliss/" rel="nofollow" target="_blank">https://www.linkedin.com/in/charlenechambliss/</a></li> <li>➡️Charlene on Twitter – <a href="https://twitter.com/blissfulchar" rel="nofollow" target="_blank">https://twitter.com/blissfulchar</a> </li> </ul> Recommendations:  <ul> <li>🎬3blue1brown – Awesome YouTube channel about math &amp; science: <a href="https://www.youtube.com/c/3blue1brown" rel="nofollow" target="_blank">https://www.youtube.com/c/3blue1brown</a> </li> <li>🎙NLP Highlights – Allen AI Insititute podcast about NLP research: <a href="https://soundcloud.com/nlp-highlights" rel="nofollow" target="_blank">https://soundcloud.com/nlp-highlights</a> </li> <li>🎙Software engineering daily: <a href="https://softwareengineeringdaily.com/" rel="nofollow" target="_blank">https://softwareengineeringdaily.com/</a> </li> <li>🎙TWiML – Another great podcast about machine learning and AI: <a href="https://twimlai.com/" rel="nofollow" target="_blank">https://twimlai.com/</a> </li> <li>📰Sebastian Ruder's blog and newsletter about NLP and ML: <a href="https://ruder.io/" rel="nofollow" target="_blank">https://ruder.io/</a> </li> <li>📰Taming the Tail: Adventures in Improving AI Economics: <a href="https://a16z.com/2020/08/12/taming-the-tail-adventures-in-improving-ai-economics/" rel="nofollow" target="_blank">https://a16z.com/2020/08/12/taming-the-tail-adventures-in-improving-ai-economics/</a> </li> <li>📰State of AI report (2021): <a href="https://www.stateof.ai/" rel="nofollow" target="_blank">https://www.stateof.ai/</a> </li> <li>📕Learn to learn – Ultralearning by Scott Young: <a href="https://www.scotthyoung.com/" rel="nofollow" target="_blank">https://www.scotthyoung.com/</a> </li> </ul> 🌐Check Out Our Website! <a href="https://dagshub.com" rel="nofollow" target="_blank">https://dagshub.com</a> Social Links:  <ul> <li>🟦LinkedIn: <a href="https://www.linkedin.com/company/dagshub" rel="nofollow" target="_blank">https://www.linkedin.com/company/dagshub</a></li> <li>🐦Twitter: <a href="https://twitter.com/TheRealDAGsHub" rel="nofollow" target="_blank">https://twitter.com/TheRealDAGsHub</a></li> <li>🐦Dean Pleban: <a href="https://twitter.com/DeanPlbn" rel="nofollow" target="_blank">https://twitter.com/DeanPlbn</a></li> </ul>

💬 MLOps for NLP Systems with Charlene Chambliss

Today we’re joined by Charlene Chambliss, Machine Learning Engineer at Primer AI.  Charlene, who we also had the pleasure of hosting at NLP Office Hours during TWIMLfest, is back to share some of the work she’s been doing with NLP. In our conversation, we explore her experiences working with newer NLP models and tools like BERT and HuggingFace, as well as whats she’s learned along the way with word embeddings, labeling tasks, debugging, and more. We also focus on a few of her projects, like her popular multi-lingual BERT project, and a COVID-19 classifier.  Finally, Charlene shares her experience getting into data science and machine learning coming from a non-technical background, and what the transition was like, and tips for people looking to make a similar shift.

Today we’re joined by Charlene Chambliss, Machine Learning Engineer at Primer AI.  Charlene, who we also had the pleasure of hosting at NLP Office Hours during TWIMLfest, is back to share some of the work she’s been doing with NLP. In our conversation, we explore her experiences working with newer NLP models and tools like BERT and HuggingFace, as well as whats she’s learned along the way with word embeddings, labeling tasks, debugging, and more. We also focus on a few of her projects, like her popular multi-lingual BERT project, and a COVID-19 classifier.  Finally, Charlene shares her experience getting into data science and machine learning coming from a non-technical background, and what the transition was like, and tips for people looking to make a similar shift.

Deep Learning for NLP: From the Trenches with Charlene Chambliss - #433