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.

Join Christopher Nuland as he revisits the thrilling world of the 1990s arcade game Double Dragon, exploring advanced techniques in distributed AI training using Kubernetes and KubeRay on OpenShift. This session dives into the application of OpenShift to deploy game simulations across a cluster, enabling rapid AI training through distributed computing. Discover how the integration of KubeRay enhances these processes, significantly reducing the time required for training reinforcement learning models like Deep Q-Network (DQN) and Proximal Policy Optimization (PPO).

Witness firsthand how these technologies are applied to train AI agents that can master complex video games, demonstrating the power and scalability of OpenShift for AI training. The talk will cover practical steps for setting up and managing distributed training environments, optimizing resource usage, and achieving faster convergence times in AI model training.

Beyond gaming, Christopher will discuss the broader implications of these techniques in fields requiring large-scale AI solutions, such as healthcare and autonomous driving. The presentation aims to empower attendees with the knowledge to leverage Kubernetes and OpenShift in their AI projects, fostering innovation and efficiency in their operations.