In this episode, I'm speaking with Julien Chaumond from 🤗 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:
- 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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