Elevate Your AI Game: Master Infrastructure with SoAI Certification

The SoAI-Certified Professional: AI Infrastructure (NCP-AII) course is tailored for advanced professionals aiming to excel in GPU-powered infrastructure for large-scale AI workloads. As the demands of AI models escalate, success hinges not just on the sophistication of algorithms but also on the strategic design, optimization, and security of the infrastructure that supports them. This comprehensive certification equips you with the knowledge to create, manage, and scale dynamic environments that promise top-tier performance and enterprise readiness.

Commencing with the essentials of AI infrastructure, you will delve into the critical functionalities of GPUs, DPUs, and CPUs and how their integration accelerates machine learning (ML) and deep learning (DL) pipelines. You’ll acquire a robust foundation in NVIDIA’s ecosystem, which is integral to contemporary AI through the exploration of CUDA programming, NVIDIA GPU Cloud (NGC) resources, and the Triton Inference Server. Additionally, hands-on experience in GPU resource management and virtualization will empower you to master Multi-Instance GPU (MIG) configuration and GPU load-balancing techniques.

The curriculum progresses to address the intricacies of storage, networking, and data pipelines, utilizing high-speed interconnects to ensure optimal data flow. You will explore real-world modules covering the deployment of Kubernetes, Helm, and Kubeflow for multi-GPU orchestration, allowing solutions tailored to varied enterprise needs. Skills in performance monitoring and optimization, focusing on tools like Nsight and TensorRT, will enhance operational efficiency while maintaining compliance with essential regulations such as GDPR and HIPAA. By course completion, you’ll be equipped to design an AI infrastructure architecture that meets rigorous enterprise requirements and poised to achieve the NCP-AII certification with confidence.

What you will 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.

Course Content:

• Sections: 11 sections
• Lectures: 50 lectures
• Duration: 3h 5m

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.

Who is it 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.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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