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
⚡ 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
The
SoAI-Certified Professional: AI Infrastructure (NCP-AII) course is designed for advanced professionals who want to
master GPU-powered infrastructure for large-scale
AI workloads. As AI models grow in complexity, success depends not just on algorithms, but on the ability to design, optimize, and secure the
AI infrastructure that powers them. This certification prepares you to build, manage, and scale cutting-edge environments that deliver
performance, efficiency, and enterprise readiness.
You'll begin with the
foundations of AI infrastructure, exploring the critical role of
GPUs,
DPUs, and
CPUs, and how they combine to accelerate
machine learning (ML) and
deep learning (DL) pipelines. From understanding
CUDA programming,
NGC (NVIDIA GPU Cloud) resources, and the
Triton Inference Server, you'll build a strong grounding in the NVIDIA ecosystem that underpins modern AI.
Next, the course dives into
GPU resource management and virtualization, where you'll gain hands-on experience with
MIG (Multi-Instance GPU) configuration,
GPU sharing and isolation, and
virtual GPU (vGPU) setup. You'll also learn how to integrate GPU workloads into
Kubernetes clusters, ensuring efficient scheduling and scalability across multi-tenant environments.
The curriculum then addresses
storage, networking, and data pipelines, covering high-speed interconnects like
NVLink,
Infiniband, and
RDMA, as well as strategies for eliminating
data movement bottlenecks. You'll design
end-to-end AI pipelines that handle
ETL, training, and inference, ensuring seamless flow from raw data to production deployment.
Building on this, you'll explore
cluster orchestration and scalability, leveraging
Kubernetes,
Helm,
Operators, and
Kubeflow to orchestrate multi-GPU workloads. You'll examine
on-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 using
Nsight,
DLProf, and
nvtop, monitor GPU metrics, and apply
TensorRT 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 implement
workload security policies, configure
role-based access control (RBAC), and integrate
DPUs with DOCA for advanced encryption and network isolation. You'll also learn how to align infrastructure with
GDPR, HIPAA, and FedRAMP standards, ensuring compliance for sensitive industries like healthcare and finance.
The course extends to
edge AI infrastructure, with modules on
NVIDIA Jetson and
Orin devices,
federated learning, and
industrial IoT deployments. You'll then master
model deployment at scale using
NGC and the
Triton Inference Server, covering multi-framework serving, load balancing, and high-availability design.
Finally, real-world case studies and a
capstone project let you design and present a full
AI infrastructure architecture that meets enterprise requirements. Through
labs, mock exams, and flashcards, you'll be fully prepared for the
NCP-AII certification exam.
By completing this program, you will gain the skills to architect, optimize, and secure
enterprise-grade AI infrastructure that supports tomorrow's most demanding workloads. This certification sets you apart as a leader in
AI 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
Код:
https://anonymz.com/?
https://www.udemy.com/course/ncp-aii-nvidia-certified-professional-ai-infrastructure
Soai-Certified Professional: Ai Infrastructure (ncp-Aii)
