In this learning path you will gain a fundamental understanding of AI security by exploring topics such as the AI development lifecycle, data flow during this lifecycle, as well as attack surfaces present in the AI development lifecycle. You will also learn about topics such as the attacks that AI models face and the risks of user data and privacy when it comes to AI.

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.

This Learning Path aims to provide a fundamental understanding of AI, from it's earliest beginnings, to current state, and its potential future developments, making it accessible to readers with varying levels of technical expertise.

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.

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 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!

Today, network engineers, especially in the data center space, must acquire AI/ML infrastructure skills and be able to discuss the required infrastructure upgrades and the reasoning for the upgrades with upper management. At WWT, we are committing $500 million to help our customers with AI/ML, and we have launched a new series of Learning Paths to help the reader navigate complex AI topics. By mastering these areas, data center network engineers can effectively contribute to successfully implementing and managing advanced AI and HPC infrastructure, aligning technological capabilities with business objectives while maintaining a robust and secure network environment.

This Learning Series was created for DGX admins and operators to explore things you would use on Day 2 when administering your DGX Superpod and Base pod 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.).

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.

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.

High-performance computing (HPC) is a rapidly evolving field that enables researchers, scientists and engineers to solve complex problems and drive innovation across various domains. As the demand for computational power continues to grow, professionals with skills in HPC are becoming increasingly valuable in today's job market. This learning path is designed to provide you with a comprehensive understanding of HPC concepts, technologies and best practices, empowering you to harness the power of supercomputers and parallel processing to tackle the most challenging computational tasks.

Explore the critical role of high-performance storage in AI infrastructure. Gain insights into storage requirements for AI/ML workloads, architectures like distributed file systems and all-flash arrays, and strategies to optimize storage for model training and inference. Stay ahead with emerging trends shaping the future of AI storage solutions.