Efficient data ingestion is foundational to modern AI-driven applications, yet developers face significant challenges: unstructured data, sensitive information management, and rising costs from excessive model fine-tuning. Fortunately, cloud-native Java runtimes like Quarkus simplify this process by seamlessly bridging data ingestion and AI workflows, primarily through Retrieval-Augmented Generation (RAG). In this hands-on technical workshop tailored for developers and AI engineers, we'll explore how Quarkus empowers teams to ingest, structure, and query data, making institutional knowledge instantly available to large language model (LLM) consumers.
Participants will:
* Structure Unstructured Data: Learn to extract actionable insights from PDFs, proprietary formats, and unstructured documents using the open-source Docling project, preparing your data for seamless AI integration.
* Deploy and Utilize RAG Effectively: Understand how RAG enables real-time retrieval and enhances generative responses without extensive fine-tuning. We’ll also cover targeted fine-tuning with InstructLab for specialized, domain-specific knowledge.
* We'll culminate the workshop by constructing a practical, privacy-conscious application: a searchable, AI-powered ticketing solution inspired by systems like ServiceNow.

Join us and discover how easily Quarkus and RAG can transform your raw data into secure, powerful, and instantly accessible business insights.

Effectively deploying Large Language Models (LLMs) in Kubernetes is critical for modern AI workloads, and vLLM has emerged as a leading open-source project for LLM inference serving. This session will explore the unique features of vLLM, which set it apart by maximizing throughput and minimizing resource usage. We’ll explore the lifecycle of deploying AI/LLM workloads on Kubernetes, focusing on achieving seamless containerization, efficient scaling with Kubernetes-native tools, and robust monitoring to ensure reliable operations.

By simplifying complex workloads and optimizing performance, vLLM drives innovation in scalable and efficient LLM deployment by leveraging features like dynamic batching and distributed serving, making advanced inference accessible for diverse and demanding use cases. Join us to learn why vLLM is shaping the future of LLM serving and how it integrates into Kubernetes to deliver reliable, cost-effective, and high-performance AI systems.