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.

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.

Kubeflow is an open-source machine learning platform dedicated to making deployments of machine learning workflows on Kubernetes simple, portable, and scalable. This learning path is structured to provide both theoretical knowledge and practical, hands-on experience with the core components of the Kubeflow ecosystem.

This learning path covers vector search from concept to practice. Articles explain vectors, embeddings, similarity metrics, and vector store software — including how to choose the right database and index type. The hands-on lab then stress-tests embedding models, compares distance metrics, evaluates models of different sizes, and builds a framework for tuning chunking and measuring retrieval quality.

This is a learning path for the introduction of the deployment and lifecycle of AI infrastructure. Explore the NVIDIA GPU Operator, a Kubernetes-native tool that automates driver installation, container runtimes, and device plugins. Perfect for DevOps engineers aiming to streamline high-performance, GPU-accelerated clusters at scale.

In this learning path, we explore the software based orechestration and management of the Cisco Secure AI Factory data center. We explore Cisco Intersight, a SaaS-baased management platform, which allows a data center engineer to orchestrate and automate physical and virtual infrastructure. We also have a lab around the Cisco Nexus Dashboard Fabric Controller.

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.