This lab will guide and educate you on what the F5 BIG-IP Next for Kubernetes is, how it is deployed with NVIDIA DOCA, and how it will ultimately help show the gains in offloading traffic to DPUs instead of tying up resources for your applications on the CPU of the physical K8 host.

The recruitment of patients for clinical trials presents significant challenges for pharmaceutical companies, often delaying drug development, increasing cost, and leaded to study failure. This lab explores how AI applied to anonymized health records enables efficient trial matching—at single, bulk, and multi-trial levels—reducing time, improving accuracy, and enhancing access for researchers, providers, and patients.

NVIDIA's AI Blueprint for RAG is a foundational guide for developers to build powerful data extraction and retrieval pipelines. It leverages NVIDIA NeMo Retriever models to create scalable and customizable RAG (Retrieval-Augmented Generation) applications. This blueprint allows you to connect large language models (LLMs) to a wide range of enterprise data, including text, tables, charts, and infographics within millions of PDFs. The result is context-aware responses that can unlock valuable insights. By using this blueprint, you can achieve 15x faster multimodal PDF data extraction and reduce incorrect answers by 50%. This boost in performance and accuracy helps enterprises drive productivity and get actionable insights from their data.

This lab will walk you through one of Intel's AI Reference Kits to develop an optimized semantic segmentation solution based on the Visual Geometry Group (VGG)-UNET architecture, aimed at assisting drones in safely landing by identifying and segmenting paved areas. The proposed system utilizes Intel® oneDNN optimized TensorFlow to accelerate the training and inference performance of drones equipped with Intel hardware. Additionally, Intel® Neural Compressor is applied to compress the trained segmentation model to further enhance inference speed. Explore the Developer Catalog for information on various use cases.

The WWT AI-Powered Predictive Maintenance Assistant uses machine learning and a large language model to predict equipment failures, schedule maintenance, generate instructions, and answer related questions. Through an interactive UI, users explore proactive, data-driven strategies to reduce downtime, extend machine life, and optimize resources, demonstrating AI's transformative potential in manufacturing environments.

Iterate.ai's Generate platform pairs with Intel Xeon CPUs, Gaudi 3 accelerators, Pure Storage FlashBlade//S, and Milvus vector DB. Deployed via Kubernetes/Slurm, it scales quickly, needs minimal tuning, and runs Llama 3, Mistral, and Inflection to accelerate AI training, inference, and search for healthcare, life-science, and finance workloads.

Explore the WWT Agentic Network Assistant, a browser-based AI tool that converts natural language into Cisco CLI commands, executes them across multiple devices, and delivers structured analysis. Using a local LLM, it streamlines troubleshooting, summarizes device health, and compares configs, demonstrating the future of intuitive, AI driven network operations.

Experience Cisco AI Defense in an interactive Capture the Flag (CTF), designed to showcase how Cisco is securing the future of GenAI.