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 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.

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.

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 walk through the components that make up VMware Private AI Foundation. We will review how VMware has tied the traditional components of VCF like SDDC Manager, vCenter, vSAN, and NSX and paired it with new components like the Data Services Manager, Harbor, and Private AI Services to deliver VMware Private AI Foundation.

Model Context Protocol (MCP) is quickly becoming the standard way AI applications connect to external tools and data. In this learning path, you'll develop a practical understanding of what MCP is, why it was created, and how it works. You'll begin by exploring the integration problem MCP solves and why an open standard matters. From there, you'll learn how MCP is structured, including the roles of hosts, clients, and servers. Next, you'll examine the core building blocks that MCP servers expose: tools, resources, and prompts. You'll learn who controls each primitive, how it is used, and when to choose one over another. By the end of the learning path, you'll be ready to explain MCP, evaluate where it fits, and apply it to your own AI projects.

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.).

Understand InfiniBand AI fabric through its lossless architecture, SHARP in-network computing, and real-world economics. Then experience a full operational lifecycle from day-zero design through UFM deployment and predictive maintenance, reinforced with hands-on lab practice. Learn how self-driving operations and InfiniBand technologies are shaping the next generation of AI factories.

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.

Welcome to the How to Pick the Right LLM Learning Path! Designed for engineers and technical practitioners, this course moves beyond benchmark-driven defaults to build a repeatable approach to model selection, rooted in a core truth: there is no single best model, only the right model for a specific task. You will first master a framework built around six key selection factors: use case, performance, latency, cost at scale, deployment, and security. From there, the path dives into the distinct LLM call types that power agentic systems—including classification, planning, and tool dispatch—and explores how architectural properties like reasoning mode and structured-output compliance dictate fit. Finally, you will jump into a hands-on JupyterLab environment to benchmark models across four capability tiers on canonical agent tasks. By measuring real-world latency and token consumption, you will build a data-driven scorecard to confidently design optimized, multi-tier model architectures.

This Learning Series was created for NVIDIA DGX admins and operators to explore things you would use on Day 3 when administering your NVIDIA DGX SuperPOD and BasePOD environments with BCM (Base Command Manager). It will go into advanced topics of cmshell, cloud bursting from BCM, HA for headnodes, IB setup and testing of worker nodes, active directory integrations, as well as advanced workload topics of deploying Kubernetes from Base Command Manager.