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SoAI– Certified Professional AI Infrastructure (NCP– AII)
SoAI– Certified Professional AI Infrastructure (NCP– AII)
Last updated 2/2026
Created by School of AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 51 Lectures ( 3h 6m ) | Size: 500.1 MB
Master GPU-powered AI infrastructure design, orchestration, security, and scalability with SoAI NCP-AII.
What you'll learn
⚡ Design and deploy GPU-powered AI infrastructure by mastering storage, networking, orchestration, and scalability strategies.
⚡ Configure and manage advanced GPU features such as MIG, vGPU, and Kubernetes scheduling to optimize multi-tenant AI workloads.
⚡ Implement performance optimization and monitoring tools like Nsight, DLProf, TensorRT, and DCGM to maximize efficiency.
⚡ Apply security, compliance, and governance frameworks (GDPR, HIPAA, RBAC, DOCA) to safeguard enterprise-grade AI infrastructure.
Requirements
❗ Basic knowledge of AI and machine learning workflows (training, inference, pipelines).
❗ Familiarity with Linux command line and system administration.
❗ Understanding of containerization (Docker, Kubernetes basics preferred).
❗ Access to a Linux server or cloud environment with an NVIDIA GPU (A100, H100, or similar) for hands-on labs.
❗ (Optional but helpful) Experience with Python scripting and working with frameworks like TensorFlow or PyTorch.
Description
TheSoAI-Certified Professional: AI Infrastructure (NCP-AII) course is designed for advanced professionals who want tomaster GPU-powered infrastructure for large-scaleAI workloads. As AI models grow in complexity, success depends not just on algorithms, but on the ability to design, optimize, and secure theAI infrastructure that powers them. This certification prepares you to build, manage, and scale cutting-edge environments that deliverperformance, efficiency, and enterprise readiness.
You'll begin with thefoundations of AI infrastructure, exploring the critical role ofGPUs,DPUs, andCPUs, and how they combine to acceleratemachine learning (ML) anddeep learning (DL) pipelines. From understandingCUDA programming,NGC (NVIDIA GPU Cloud) resources, and theTriton Inference Server, you'll build a strong grounding in the NVIDIA ecosystem that underpins modern AI.
Next, the course dives intoGPU resource management and virtualization, where you'll gain hands-on experience withMIG (Multi-Instance GPU) configuration,GPU sharing and isolation, andvirtual GPU (vGPU) setup. You'll also learn how to integrate GPU workloads intoKubernetes clusters, ensuring efficient scheduling and scalability across multi-tenant environments.
The curriculum then addressesstorage, networking, and data pipelines, covering high-speed interconnects likeNVLink,Infiniband, andRDMA, as well as strategies for eliminatingdata movement bottlenecks. You'll designend-to-end AI pipelines that handleETL, training, and inference, ensuring seamless flow from raw data to production deployment.
Building on this, you'll explorecluster orchestration and scalability, leveragingKubernetes,Helm,Operators, andKubeflow to orchestrate multi-GPU workloads. You'll examineon-premises, cloud, and hybrid cluster topologies, enabling you to deploy flexible solutions tailored to enterprise needs.
Performance optimization is another core focus. You'll learn how to profile GPU workloads usingNsight,DLProf, andnvtop, monitor GPU metrics, and applyTensorRT optimization to accelerate inference. The course emphasizes identifying bottlenecks, tuning systems, and ensuring workloads run at maximum efficiency.
Security and compliance are critical in enterprise AI. You'll implementworkload security policies, configurerole-based access control (RBAC), and integrateDPUs with DOCA for advanced encryption and network isolation. You'll also learn how to align infrastructure withGDPR, HIPAA, and FedRAMP standards, ensuring compliance for sensitive industries like healthcare and finance.
The course extends toedge AI infrastructure, with modules onNVIDIA Jetson andOrin devices,federated learning, andindustrial IoT deployments. You'll then mastermodel deployment at scale usingNGC and theTriton Inference Server, covering multi-framework serving, load balancing, and high-availability design.
Finally, real-world case studies and acapstone project let you design and present a fullAI infrastructure architecture that meets enterprise
Requirements
. Throughlabs, mock exams, and flashcards, you'll be fully prepared for theNCP-AII certification exam.By completing this program, you will gain the skills to architect, optimize, and secureenterprise-grade AI infrastructure that supports tomorrow's most demanding workloads. This certification sets you apart as a leader inAI infrastructure engineering.
Who this course is for
⭐ AI Engineers & Data Scientists who need to scale their training and inference pipelines on high-performance NVIDIA GPUs.
⭐ System Administrators & DevOps Engineers responsible for managing GPU clusters, Kubernetes workloads, and monitoring performance.
⭐ Cloud Architects & Infrastructure Specialists designing hybrid, cloud, or edge AI infrastructure solutions.
⭐ IT Managers & Technical Leaders seeking to ensure security, compliance, and efficiency in enterprise AI deployments.
⭐ Professionals preparing for the NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) credential to validate their skills.
Homepage
https://www.udemy.com/course/ncp-aii-nvidia-certified-professional-ai-infrastructure
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