Ines & Sofie — Building Industrial-Strength NLP Pipelines

Ines & Sofie — Building Industrial-Strength NLP Pipelines

Sofie and Ines walk us through how the new spaCy library helps build end to end SOTA natural language processing workflows.

Ines Montani is the co-founder of Explosion AI, a digital studio specializing in tools for AI technology. She's a core developer of spaCy, one of the leading open-source libraries for Natural Language Processing in Python and Prodigy, a new data annotation tool powered by active learning. Before founding Explosion AI, she was a freelance front-end developer and strategist.

https://twitter.com/_inesmontani


Sofie Van Landeghem is a Natural Language Processing and Machine Learning engineer at Explosion.ai. She is a Software Engineer at heart, with an absurd love for quality assurance and testing, introducing proper levels of abstraction, and ensuring code robustness and modularity.


She has more than 12 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry.

https://twitter.com/oxykodit


https://spacy.io/

https://prodi.gy/

https://thinc.ai/

https://explosion.ai/


Topics covered:

0:00 Sneak peek

0:35 intro

2:29 How spaCy was started

6:11 Business model, open source

9:55 What was spaCy designed to solve?

12:23 advances in NLP and modern practices in industry

17:19 what differentiates spaCy from a more research focused NLP library?

19:28 Multi-lingual/domain specific support

23:52 spaCy V3 configuration

28:16 Thoughts on Python, Syphon, other programming languages for ML

33:45 Making things clear and reproducible

37:30 prodigy and getting good training data

44:09 most underrated aspect of ML

51:00 hardest part of putting models into production


Visit our podcasts homepage for transcripts and more episodes!

www.wandb.com/podcast


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We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!


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