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Abstract
Generative AI usage has increased exponentially since start of the year and has created tremendous opportunities from startups to large enterprises. As more and more LLMs are released for research and commercial use, it becomes complex for enterprises to adopt the LLMs either using a managed service offering or even hosting it in-house as the cost is extremely high. This paper will focus on helping companies to optimize LLM, provide examples of use cases and solutions on fine tuning, cost optimizations, hosting LLM models internally in Kubernetes to solve data privacy, security and governance risks.
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