This lab is intended to give you an overview of HPE Private Cloud AI, HPE's turnkey solution for on-prem AI workloads. HPE has paired with NVIDIA's AI Enterprise software giving customers a scalable on-prem solution that can handle multiple different AI workloads from Inference, RAG, model fine-tuning, and model training.

Compare large language models side by side using WWT's AI Proving Ground GPU cluster. Send the same prompt to multiple models at once and evaluate their responses in real time — measuring response speed, reasoning quality, and output style across leading open-source and proprietary models. No setup required. The lab environment connects automatically to the LLMaaS AI Gateway, giving you direct access to production-grade inference endpoints used by WWT's AI engineering teams.

In this lab we will explore F5 AI Guardrails which is a runtime security platform for generative AI and agentic AI systems, focused on securing the AI inference layer—the point where models receive prompts and generate outputs.

Explore the hidden dangers of prompt injection in Large Language Models (LLMs). This lab reveals how attackers manipulate LLMs to disclose private information and behave in ways that they were not intended to. Discover the intricacies of direct and indirect prompt injection and learn to implement effective guardrails.

Explore the Securing AI with CrowdStrike Lab, where AI security meets real-world challenges. Discover how CrowdStrike Falcon AI Detection and Response (AIDR) and Falcon Cloud Security protect against AI-specific threats like prompt injection and data leakage, ensuring robust security for workforce, development, and runtime environments. Uncover the future of AI security today.

This lab will guide and educate you on what the F5 BIG-IP Next Cloud-Native Network Functions are, how they are deployed with NVIDIA DOCA, and how they 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.

Routing LLM Requests with NVIDIA Switchyard teaches config-driven LLM routing through a hands-on Docker Compose lab. Students deploy an OpenAI-compatible proxy plus dashboard, use passthrough routes, binary and category-based LLM classifiers, then measure real cost, latency, and quality tradeoffs between routed and frontier-baseline models across six progressive modules.

This hands-on lab provides a comprehensive introduction to NVIDIA Run:ai, a powerful platform for managing AI workloads on Kubernetes. Designed for AI practitioners, data scientists, and researchers, this lab will guide you through the core concepts and practical applications of Run:ai's workload management system.

In this lab you will explore an NVIDIA Data Flywheel running on Cisco Secure AI Factory hardware with NVIDIA in WWT's AI Proving Ground. Built on the NVIDIA NeMo microservices platform, the flywheel automatically turns production traffic into evaluation and fine-tuning datasets, LoRA fine-tunes candidate models with NeMo Customizer, scores them against the incumbent with NeMo Evaluator, and deploys the winner as an NVIDIA NIM. Using a clinical coding application as the example workload, you will watch a 1-billion-parameter model close the accuracy gap on a 70-billion-parameter model at a fraction of the inference cost.

Explore how InfiniBand fabrics power modern AI clusters through a guided tour of NVIDIA Unified Fabric Manager (UFM). Learn core InfiniBand concepts, RDMA, routing, telemetry, and fabric automation while examining a live GPU-focused environment. This read-only lab focuses on observing topology, health, performance, and management workflows used in large-scale AI and HPC deployments.

In this lab a customer will deploy the Patch & Vulnerability Prioritization Agent on the HPE Private Cloud AI instance in the ATC. This Assistant is built on top of the NVIDIA NeMo Agent Toolkit and leverages Open AI OSS NIMs under the hood. Leveraging representative data from an IT Service Management Platform, the Patch & Vulnerability Prioritization Agent

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.