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Inside LLMaaS: the partner stack behind model-as-a-service in AI Proving Ground
How WWT's LLMaaS gives builders one governed, OpenAI-compatible endpoint to 40+ models, and the partner stack behind it, layer by layer.
NVIDIA Data Flywheel + Cisco Secure AI Factory
In this lab you will explore an NVIDIA Data Flywheel running on Cisco Secure AI Factory hardware with NVIDIA in WWT's AI Proving Ground. Built on the NVIDIA NeMo microservices platform, the flywheel automatically turns production traffic into evaluation and fine-tuning datasets, LoRA fine-tunes candidate models with NeMo Customizer, scores them against the incumbent with NeMo Evaluator, and deploys the winner as an NVIDIA NIM. Using a clinical coding application as the example workload, you will watch a 1-billion-parameter model close the accuracy gap on a 70-billion-parameter model at a fraction of the inference cost.
ATC+
NVIDIA Run:ai Researcher Sandbox
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.
Retrieval Augmented Generation (RAG) Walk Through Lab
This lab will go into the basics of Retrieval Augmented Generation (RAG) through hands on access to a dedicated environment.
Pre-Visit Planning Tool Demo
Empowering clinicians with better data visibility while improving communication and experience for patients.
AI Prompt Injection Lab
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.
NVIDIA Run:ai for Platform Engineers
Welcome to the NVIDIA Run:ai for Platform Engineers Learning Path! This learning path is designed to build both foundational knowledge and practical skills for platform engineers and administrators responsible for managing GPU resources at scale. It begins by introducing learners to the key components of the NVIDIA Run:ai platform, including its Control Plane and Cluster, and explains how NVIDIA Run:ai extends Kubernetes to orchestrate AI workloads efficiently. The learning path then covers essential topics such as authentication and role-based access, organizational management through projects and departments, and workload operations using assets, templates, and policies. Learners will also explore GPU fractioning to understand how NVIDIA Run:ai maximizes GPU utilization and ensures fair resource allocation across teams. All this builds toward a hands-on lab experience designed to reinforce your learning and give you practical experience working directly with NVIDIA Run:ai.