🤗 Large ML models in production with HuggingFace CTO Julien Chaumond

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. Join our Discord community: https://discord.gg/tEYvqxwhah --- 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 --- 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