In this lab, you will have access to a deployment of an AMD blueprint for a Financial Stock Intelligence Agent. It is deployed on AMD Instinct MI250X instance in the ATC. Built using LangChain and powered by AMD Inference Microservices (AIM), this solution combines real-time market data, technical indicators, and large language model analysis to deliver AI-generated stock insights through an interactive web interface. Financial analysts and developers can explore how AMD's open AI stack accelerates inference for financial workflows.

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.

Get hands-on with Claude across three access points (the Claude.ai web app, Claude Desktop with Cowork, and the Claude Code CLI). Through four guided exercises on real IT scenarios, you'll run a multi-turn conversation, build a Project, delegate work to Cowork, and use Claude Code to find a bug, learning which tool to reach for when.

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.

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.

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.

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 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.