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.
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.
Your Instructor
School of AI
AI Academy
4.4 Instructor Rating
8,562 Reviews
328,933 Students
86 Courses
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