NVIDIA GPU AI Factory: Architecture, Engineering & Operations
Master NVIDIA GPU Clusters, GB300 NVL72, InfiniBand, RDMA, High-Speed Networking, Kubernetes, Run and Enterprise AI Infrastructure.
Prepares you for: AI Infrastructure Engineer, GPU Infrastructure Engineer, HPC Engineer, Cloud Infrastructure Architect, AI Platform Engineer
What you'll learn
- Design enterprise AI infrastructure using NVIDIA GPU clusters and rack-scale architectures.
- Understand Hopper, Blackwell, GB300 NVL72, NVLink and NVSwitch technologies.
- Engineer high-speed AI networks using InfiniBand, RoCEv2, RDMA and lossless Ethernet.
- Design GPU cluster topologies, rail-optimized fabrics and enterprise AI data centres.
- Implement power infrastructure, liquid cooling and high-performance AI storage architectures.
- Provision GPU clusters using Base Command Manager, CUDA and NVIDIA software components.
- Orchestrate GPU workloads using Slurm, Kubernetes, GPU Operator and Run:ai.
- Monitor, troubleshoot, validate and operate production-grade AI infrastructure.
Skills you'll gain
Tools you'll use
Course content 1 modules · 2 topics · 2h 16m total
▸Module 01: AI, Machine Learning & Deep Learning FoundationsUnderstand AI, Machine Learning, Deep Learning and Generative AI fundamentals, including learning approaches, neural network architectures, model training concepts and their relationship to GPU computing.2 topics · 2h 16m
About this course
Enterprise AI Factory: Architecture, Engineering & Operations is a comprehensive, industry-oriented technical course designed to help infrastructure professionals understand, design and operate next-generation enterprise AI infrastructure.
As organizations scale AI workloads, traditional computing and networking architectures are evolving into high-performance GPU-powered infrastructure. This course explores the engineering principles, technologies and operational practices required to build production-ready AI factories.
Starting with AI workloads, GPU computing fundamentals and distributed training architectures, you will progress into NVIDIA Hopper and Blackwell GPU platforms, GB300 NVL72 rack-scale architecture, NVLink, NVSwitch, InfiniBand, Spectrum-X Ethernet, RoCEv2 and RDMA networking.
The curriculum covers high-speed AI fabric design, network topologies, lossless Ethernet, congestion control, power infrastructure, liquid cooling, parallel storage, checkpointing and enterprise AI data-centre architecture.
You will also explore NVIDIA's software ecosystem, including CUDA, NGC, NVIDIA AI Enterprise, GPU Operator, Base Command Manager, Slurm, Kubernetes, Multus CNI and Run:ai for GPU cluster provisioning and workload orchestration.
The course concludes with GPU observability, DCGM monitoring, Prometheus, Grafana, production troubleshooting, cluster validation and acceptance procedures.
With 23 in-depth modules and 10 hands-on HPC cluster labs outlined in the program, learners gain exposure to real-world infrastructure engineering scenarios and practical operational workflows.
**Learn to design the infrastructure. Engineer the GPU fabric. Orchestrate workloads. Operate enterprise AI factories.**
Ideal for Cloud Engineers, DevOps Professionals, HPC Engineers, Network Engineers, AI Infrastructure Specialists and Platform Architects who want to advance into enterprise AI infrastructure engineering.
Requirements
- Basic knowledge of Linux command-line operations, file systems and Bash scripting.
- Understanding of IP addressing, subnets, VLANs, routing and the OSI networking model.
- Familiarity with server administration and infrastructure concepts is recommended.
- Basic Docker and Kubernetes awareness is helpful but not mandatory.
- No prior InfiniBand, AI, deep learning or Python experience is required.
- A willingness to explore GPU infrastructure, high-performance networking and hands-on engineering is essential.
Who this course is for
- Cloud and DevOps Engineers transitioning into enterprise AI infrastructure.
- Linux and Systems Administrators managing high-performance computing environments.
- Network Engineers interested in InfiniBand, RDMA and high-speed AI fabrics.
- Data Centre Engineers working with GPU infrastructure, power and liquid cooling.
- AI/ML and MLOps Professionals exploring GPU cluster architecture and operations.
- Technical Architects designing enterprise AI factories and GPU-powered infrastructure.
Frequently asked questions
What is the Enterprise AI Factory course about?
This course teaches you how to design, engineer, deploy and operate enterprise-grade AI infrastructure using NVIDIA GPU platforms, high-speed networking, InfiniBand, RDMA, Kubernetes, Slurm and Run.
Do I need prior AI or machine learning experience?
No. Prior AI, deep learning or Python experience is not required. However, basic Linux command-line and networking knowledge is expected.
Is this course suitable for DevOps and Cloud Engineers?
Yes. It is designed for DevOps engineers, cloud engineers, infrastructure architects, Linux administrators, network engineers and professionals transitioning into AI infrastructure engineering.
Will I get hands-on practical experience?
The program syllabus includes 10 hands-on HPC cluster labs covering GPU inspection, NCCL benchmarking, RDMA validation, lossless networking, storage benchmarking, Kubernetes GPU scheduling and troubleshooting.
Which NVIDIA technologies will be covered?
The curriculum covers NVIDIA GPU architectures, including Hopper and Blackwell, GB300 NVL72, NVLink, NVSwitch, ConnectX SuperNICs, BlueField DPUs, CUDA, MIG, NGC and NVIDIA AI Enterprise technologies.
Will I learn InfiniBand, RoCE and RDMA?
Yes. You will study high-speed AI networking, InfiniBand, RoCEv2, RDMA transports, PFC, ECN, DCQCN, congestion management, network topologies and fabric troubleshooting.
Does this course help with NVIDIA certifications?
The syllabus includes preparation support for NVIDIA NCA-AIIO, NCP-AIN and NCP-AII certifications. Certification examinations and credentials are separate.
What career opportunities can this course support?
The skills are relevant to AI Infrastructure Engineer, GPU Cluster Engineer, HPC Engineer, AI Platform Engineer, Cloud Infrastructure Architect and Data Centre Infrastructure Engineer roles.
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