LLMOps: Everything You Need to Know to Manage LLMs

With the recent surge in popularity of ChatGPT and other LLMs such as Dolly, many people are going to start training, tuning, and deploying their own custom models to solve their domain-specific challenges. When training and tuning these models, there are certain considerations that need to be accounted for in the MLOps process that differ from traditional machine learning. Come watch this session where you’ll gain a better understanding of what to look out for when starting to enter the world of applying LLMs in your domain.
In this session, you’ll learn about:
- Grabbing foundational models and fine-tuning them
- Optimizing resource management such as GPUs
- Integrating human feedback and reinforcement learning to improve model performance
- Different evaluation methods for LLMs
Talk by: Joseph Bradley and Eric Peter
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[@spicoflorin6901]

Great presentation and demo. Useful information about MLops for LLM. It will be very helpful for the community if you can share the notebook and the slides.

[@IgorPolubarev]

Very usefull information. How can I get notebook from it?

[@tronoaillm]

can share slides please? Thank you so much

[@niting123]

Can you please share notebook used in this demo?

[@fintech1378]

OpenAI has all these features now?