Explore the NetApp AIPod Mini integrated with Intel® AI for Enterprise RAG. This lab automates the deployment of a secure, scalable ChatQ&A pipeline on Kubernetes. Leverage Intel® Xeon® and Gaudi® accelerators to transform enterprise data into insights, featuring one-click deployment, hardware optimization, and comprehensive observability for production-ready AI 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.

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.

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.

Welcome to the "NVIDIA Run:ai for Platform Administrators" lab, a hands-on lab designed to help you explore the architecture and operational workflows behind NVIDIA Run:ai. Whether you're a platform engineer, systems administrator, or just someone interested in the NVIDIA Run:ai platform, this lab will walk you through the core concepts that power NVIDIA Run:ai and give you the practical skills to deploy and manage the NVIDIA Run:ai platform.

In this lab a customer will deploy a NVIDIA NIM to the HPE Private Cloud AI instance in the ATC. This lab will not only walk the customer through how to deploy a black-forest-labs/FLUX.1-dev NIM (image generation NIM), but also how to interact with the kubernetes layer (Ezmeral) that is engraved in the HPE solution.

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

This hands-on lab covers the mechanics of vector search across four notebooks, each focused on a different factor that affects retrieval quality. The early notebooks examine how embedding models handle difficult language—negation, sarcasm, idioms, and domain jargon—and how the choice of distance metric and embedding model can change what a vector store returns for the same query. The final notebook introduces a repeatable framework for evaluating how chunking configurations affect retrieval accuracy, which can be adapted to your own data and pipelines. No prior vector search experience is required, and all experiments run locally using open-source models and FAISS.

This lab provides the learner with a hands-on, guided experience of deploying the NVIDIA NIM for LLM microservice in Docker.

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.

NVIDIA NIM revolutionizes AI deployment by encapsulating large language models in a scalable, containerized microservice. Seamlessly integrate with Kubernetes for optimized GPU performance, dynamic scaling, and robust CI/CD pipelines. Simplify complex model serving, focusing on innovation and intelligent feature development, ideal for enterprise-grade AI solutions.

This hands-on lab teaches enterprise engineers how to systematically evaluate, benchmark, and select optimal large language models for distinct steps inside an agentic control plane.