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.

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

In the current AI market, the demand for scalable and secure deployments is increasing. Public cloud providers (AWS, Google, and Microsoft) are competing to provide GenAI infrastructure, driving the need for multi-cloud and hybrid cloud deployments. However, distributed deployments come with challenges, including: Complexity in managing multi-cloud environments. Lack of unified visibility across clouds. Inconsistent security and policy enforcement. F5 Distributed Cloud provides a solution by offering a seamless, secure, and portable environment for GenAI workloads across clouds. This lab will guide you through setting up and securing GenAI applications with F5 Distributed Cloud on AWS EKS and GCP GKE.

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.

This lab will go into the basics of Retrieval Augmented Generation (RAG) through hands on access to a dedicated environment.

MCP is an open-source standard designed to standardize how LLM applications connect to and work with external tools and data sources.

This lab presents a simplified two-switch high-availability design for a Cisco AI deployment, highlighting both North–South and East–West traffic flows. It uses NDFC to demonstrate operational visibility and telemetry monitoring. The provided NDFC account is read-only for demonstration purposes. The topology separates administrative connectivity and GPU communication, offering a focused view of core AI networking concepts.

This lab will walk the lab user through the basics of Generative AI

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.

Explore the transformative potential of AI in cybersecurity with an AI-powered assistant that automates threat analysis. By integrating real-time data from CrowdStrike, this lab showcases AI's ability to enhance security operations, offering insights and recommendations that accelerate investigations and improve decision-making in security operations centers.

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.

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