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.

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.

This hands-on lab for LangChain 1.0 progresses from basic LLM calls and prompt templates through conversation memory, Retrieval-Augmented Generation (RAG), and autonomous agents with tools. By the end, you'll have the foundational patterns to build chatbots, document Q&A systems, and intelligent assistants that can reason over your own data.

Training data poisoning poses significant risks to Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems. This lab explores these dangers through a case study of an online forum, demonstrating how corrupted data can compromise AI effectiveness and security, and examines methods to mitigate such threats.

In this lab you will interact with HPE VM Essentials. You will be able to log into a running VME cluster, deploy VME Manager, and get a look and feel for the virtualization software that helps deliver HPE Private Cloud AI.

In this lab, customers will deploy the Intelligent Resource Optimizer on the HPE Private Cloud AI instance in the ATC. Built using the NVIDIA NeMo Agent Toolkit and powered by Open AI OSS NIMs, this solution moves beyond static analysis to proactive infrastructure management. By integrating a specialized Time Series model, the Intelligent Resource Optimizer analyzes historical system utilization to accurately forecast future resource demands. This allows IT Operations teams to receive data-driven recommendations for scaling and optimization, ensuring peak performance and cost-efficiency across the environment.

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

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.