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The exponentially growing AI/ML and LLMs advancements bring concerns about privacy, as there is a risk of data exposure to online LLMs service providers. Setting up LLMs in-house requires a high computational cost which is a major obstacle for businesses across various sectors such as Retail, Healthcare, Finance, etc. These industries seek to leverage the power of LLMs to drive profitability in their overall business while maintaining control over their data.

In this session, we will explore the Edge Ecosystem Analytics and its transformative potential in GenAI Applications through seamless orchestration via openSUSE Leap and Rancher-managed Kubernetes. This approach helps overcome challenges in adopting and deploying cutting-edge GenAI applications securely and efficiently at the edge.

Key Topics:

• Overview of Large Language Models (LLMs)
• Scope for Edge Computing in AI revolution
• Benefits over privacy concerns by localization of LLMs
• Real-world Application Showcase by leveraging GenAI for Edge Ecosystem Analytics
• Integration of Retrieval-Augmented Generation (RAG) Pipeline into Rancher & K3s
• Challenges while deploying GenAI applications at the Edge

This short talk will showcase a real-world GenAI-based application, highlighting the utilization of the RAG pipeline as well as a data modeling pipeline to continually improve analytic outputs and its seamless integration with Rancher and K3s. Attendees will learn about Rancher and K3s in managing Kubernetes deployments for GenAI applications, LLM optimization techniques such as RAG, overview of Fine Tuning and AI Agents.