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Session Name: LLMOPs: Running LLMs on Scale

With rise in availability of open source large language model, businesses wants to include them in their process to use the power of LLMs and drive business values from them, their are unlimited business use case. Building small POCs and examples are easy with LLMs. But when it comes to deploy the model at scale, things become harder. In this session we will see what open source community offer us in this direction to do this heavy lifting.

We will cover the following content:
1. Brief on Large Language Models.
2. What is LLMOPs, What are its Key Principles.
3. How LLMOPs is Different from MLOps
4. How We Can Leverage LLMOps to Scale LLMs Using Open Source Community Tools
5. Challenges We Can Face While Indulging in LLMOps

Learning goal:
1. LLMOps for Scaling LLMs at Scale

Speaker Bio:

Akash Pandey is an experienced Machine learning Engineer at Xenonstack. Akash specialises in providing advanced and scalable AI solutions to businesses and is responsible for execution of AI operations at xenon stack. During his tenure he has been part of many AI driven solutions for various business use cases. Apart from this Akash enjoys contributing with his skills in technological advancement and experiments that are part of daily life at xenon stack.