Implementing Private Large Language Models for In-House AI Solutions 

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This session is a practical overview of implementing private LLMs. You'll get a fundamental understanding of LLMs and how private versions differ from public models. We'll also explain why LLMs shouldn't be the only ingredient in your AI strategy, and other factors that play a role in LLM integration and the overall success of AI strategy.
We'll spend a few minutes discussing and taking Q&A on:
● Data privacy, security, and regulatory compliance concerns
● Scalability of AI solutions
● Managing costs
● Identifying opportunities for efficiency
● Pitfalls, challenges, caveats, and KBYG
We'll discuss infrastructure needs and customization, basic architecture considerations, and how to approach integration of these systems within existing IT environments. We'll give some realistic scenarios and use cases, providing several real-work case study examples from our research. This is particularly beneficial for project managers and strategy developers, offering practical insights into applying these models effectively in their own organizational contexts.
The discussion will also benefit decision-makers planning to expand their AI capabilities to realize new value creation opportunities, innovation, efficiency, and sustainability.