RAG, or Retrieval-Augmented Generation, is an AI solution that has gained popularity due to its ability to combine generative AI with external data sources to provide more accurate and up-to-date responses. However, these new abilities don't come without risk. In this learning path, you will gain a fundamental understanding of RAG security. Through a series of videos, you will explore topics such as RAG security risks, vector database security risks, and the best practices that can be used to help remediate some of these risks. Finally, you will take a look at all of it put together in the hands-on Training Data Poisoning lab.

Generative AI is a cutting-edge technology that has been making waves across various industries in recent years. Unlike traditional AI systems that are designed to analyze and interpret existing data, generative AI has the remarkable ability to create entirely new content, from text and images to music and even code. This revolutionary technology leverages complex machine learning algorithms and neural networks to understand patterns and structures in vast amounts of data, enabling it to generate original and often highly realistic outputs. As generative AI continues to evolve and improve, it promises to transform creative processes, problem-solving, and innovation across numerous fields, from art and entertainment to scientific research and product development. Learn about what Generative AI is and how it works, in this Learning Path.

Dive into the fascinating world of AI Agents in this comprehensive learning path that transforms beginners into creators. You'll explore what agents truly are and discover the inner workings that make them tick, from decision-making algorithms to execution frameworks. The journey continues with an in-depth look at the tools agents leverage and tasks they excel at, preparing you for the hands-on lab where you'll build your very own agent from scratch. By the end, you'll have both theoretical knowledge and practical experience in creating these powerful digital assistants that are revolutionizing how we interact with technology and solve complex problems.

The Artificial Intelligence (AI) Driver's License is a new WWT initiative aimed at ensuring we are using AI safely and effectively for our customers. The Driver's License is a way for us to validate and certify that our employees have mastered basic AI competencies and are leveraging current AI best practices in all of our client engagements. This is WWT's public version of the AI Drivers License learning path and is encouraged to be shared with others!

In this learning path, you will gain a fundamental understanding of Large Language Model (LLM) security. Through a series of articles and videos you will explore topics like what is an LLM, data security risks, the OWASP Top 10 for LLMs, strategies for defending LLM systems, and the critical role users play in maintaining security.

In this learning path, we cover NVIDIA's DGX systems and BasePOD infrastructure, detailing the setup, licensing, and management of Base Command Manager and DGX OS for high-performance AI workloads. They explain hardware requirements, network configurations, and system provisioning, emphasizing efficient resource management, scalability, and optimized AI model training across NVIDIA's cutting-edge computing platforms.

This Learning Series was created for NVIDIA DGX admins and operators to explore things you would use on Day 2 when administering your NVIDIA DGX SuperPOD and BasePOD environments with BCM (Base Command Manager). It will detail how to update firmware, patch systems, run jobs against the infrastructure, and integrate other parts into BCM (Switches, AD, Cloud, etc.).

RAG, or Retrieval-Augmented Generation, is an AI solution that combines the power of Generative AI with external data sources to provide more accurate and up-to-date responses. This Learning Path explains the concept of RAG, its origins, and how it addresses key limitations of traditional GenAI systems. You'll learn about the benefits of RAG, including enhanced accuracy, reduced hallucinations, and its flexibility in integrating real-time information without the need for constant model retraining.

NVIDIA AI Enterprise (NVAIE) offers a robust suite of AI tools for various applications, including reasoning, speech & translation, biomedical, content generation, and route planning. It features community, NVIDIA, and custom models. NVAIE provides essential microservices such as NIM and CUDA-X used for security advisory, enterprise support, cluster management, and infrastructure optimization. Designed for cloud, data centers, workstations, and edge environments, NVAIE ensures scalable, secure, and efficient AI deployment.

The user of this learning path will learn the components of RoCE and why it is essential for clean, fast, and reliable AI/ML compute communication.

In this learning path we will take you through HPE Private Cloud AI or HPE PCAI. We will guide you through all the components that make up the solution such as HPE GreenLake, Private Cloud AI, HPE Morpheus VM Essentials, GreenLake for Files storage array, HPE Ezmeral Container Platfrom and Aruba/NVIDIA switches. We will also allow you to interact with some hands on labs that will take you into both of our physical HPE Private Cloud AI environments including a small and medium setup.

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.