Learn to deploy the Nous Research Hermes Agent, backed by NVIDIA Nemotron-3-Super-120B-A12B, as a persistent infrastructure co-pilot. This hands-on lab guides you through configuring persistent memory across multiple surfaces (the terminal, Mattermost, and a desktop app), enforcing custom engineering standards, and compiling repeatable infrastructure workflows into reusable skills.

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.

Watch a multi-agent AI system run network operations on a live Cisco leaf-spine fabric. It detects configuration drift against a golden baseline, diagnoses root cause from live telemetry, and proposes Ansible remediation — but executes nothing without your approval. Work five realistic incidents, from a fat-finger mistake to a deliberate attack, and judge where agentic automation belongs.

A hands-on lab where learners configure LiteLLM as a local reverse proxy that intelligently routes requests between multiple LLM backends — a general model for simple prompts, a coding model and a reasoning model for complex ones — instead of hard-coding a single model into an application.

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

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.

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.

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.

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.

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.

The learner is guided through the installation, configuration, and operations of the Ansible Dev Tools MCP Server. The lab guide walks through using the Ansible Dev Tools MCP server to build an ansible role, validate it against ansible-lint rules and best practices, and ultimately publish it as trusted content.