Iterate.ai's Generate platform pairs with Intel Xeon CPUs, Gaudi 3 accelerators, Pure Storage FlashBlade//S, and Milvus vector DB. Deployed via Kubernetes/Slurm, it scales quickly, needs minimal tuning, and runs Llama 3, Mistral, and Inflection to accelerate AI training, inference, and search for healthcare, life-science, and finance workloads.

Experience Cisco AI Defense in an interactive Capture the Flag (CTF), designed to showcase how Cisco is securing the future of GenAI.

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'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.

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.

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.

In this lab, we'll be focusing on the programatic steps of Retrieval Augmented Generation (RAG). First, we'll discuss data chunking, how we break down our documents. Then, we'll explore how these chunks become embeddings, numerical representations. Finally, we'll see how a vector database helps us efficiently retrieve this information.

Red Hat OpenShift AI, formerly known as Red Hat OpenShift Data Science, is a platform designed to streamline the process of building and deploying machine learning (ML) models. It caters to both data scientists and developers by providing a collaborative environment for the entire lifecycle of AI/ML projects, from experimentation to production. In this lab, you will explore the features of OpenShift AI by building and deploying a fraud detection model. This environment is built ontop of Dell R660's and Intel Xeon's 5th generation processors.

This lab will demonstrate how the Cisco Nexus Dashboard Fabric Controller can easily set up an AI/ML fabric with a simple point-and-click GUI. It will only do this easily without knowing the protocols or best practices. The controller will do the work.

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

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 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.