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.

This learning path introduces NVIDIA NIM for LLM microservices, covering its purpose, formats, and benefits. You'll explore deployment options via API Catalog, Docker, and Kubernetes, and complete hands-on labs for Docker and Kubernetes-based inference workflows—building skills to deploy, scale, and integrate GPU-optimized LLMs into enterprise applications.

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.