The Future of Transformer Libraries

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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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