Name: SoAI-Certified Professional: AI Infrastructure (NCP-AII)
Availability: InStock

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.

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.

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