In this episode, I'm speaking with Julien Chaumond from đ€ HuggingFace, about how they got started, getting large language models to production in millisecond inference times, and the CERN for machine learning.
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Timestamps:Â
01:00 - Guest intro
02:14 - Origin of HuggingFace
05:37 - Why the focus on NLP?
07:45 - The success of the HuggingFace community
13:14 - Reproducing models and scaling for the community
18:14 - Enabling large models in production
23:14 - How HuggingFace scales so many models
27:34 - The biggest challenge HuggingFace solved in MLOps
32:02 - How HuggingFace transitions from research to production
34:44 - Using notebooks vs python modules
38:27 - The most interesting topic in ML production
40:10 - Fascinating ML research
45:24 - Learning new things
51:14 - Something that is true but most people disagree with
56:54 - Tips to organize research teams
1:00:05 - New features for accelerated inference
1:01:35 - Most common use case of HuggingFace
1:04:17 - Integrating search algorithms into transformer library
1:05:09 - Integrating vision models
1:06:06 - Long term business model
1:10:55 - Automation and simplification of the process of building models
1:13:02 - Support for real-time inference
1:14:40 - Recommendations for the audience
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Relevant Links:
FastDS: https://github.com/DAGsHub/fds
BigScience: https://bigscience.huggingface.co
https://www.linkedin.com/company/dagshub/
https://www.linkedin.com/company/huggingface/
https://twitter.com/TheRealDAGsHub
https://twitter.com/huggingface
