| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction to NVIDIA-Certified Professional AI Infrastructure (NCP-AII) | |||
| 1. Certificate of Completion.en_US.srt | 716.8 B | ||
| 1. Certificate of Completion.mp4 | 10.9 MB | ||
| 10 - Module 9 Real-World Projects and Enterprise Workflows | |||
| 1. Quiz Module 9 Real-World Projects and Enterprise Workflows.html | 23.4 KB | ||
| 11 - Module 10 Final Capstone + Certification Prep | |||
| 2 - Module 1 Foundations of AI Infrastructure | |||
| 3 - Module 2 GPU Resource Management and Virtualization | |||
| 10. Virtual GPUs (vGPU) Setup and Use Cases.en_US.srt | 5.9 KB | ||
| 10. Virtual GPUs (vGPU) Setup and Use Cases.mp4 | 13.7 MB | ||
| 11. GPU Workload Scheduling with Kubernetes.en_US.srt | 5.9 KB | ||
| 11. GPU Workload Scheduling with Kubernetes.mp4 | 13.2 MB | ||
| 12. Hands-on Lab Configure MIG on A100.html | 5.6 KB | ||
| 12. Lab2.pdf | 208.5 KB | ||
| 4 - Module 3 Storage, Networking, and Data Pipelines for AI | |||
| 13. Storage Architectures for AI Workloads (local, shared, object).en_US.srt | 5.3 KB | ||
| 13. Storage Architectures for AI Workloads (local, shared, object).mp4 | 15.1 MB | ||
| 14. High-Speed Networking NVLink, Infiniband, RDMA.en_US.srt | 5.7 KB | ||
| 14. High-Speed Networking NVLink, Infiniband, RDMA.mp4 | 13.5 MB | ||
| 15. Data Movement Bottlenecks and Optimization.en_US.srt | 5.6 KB | ||
| 15. Data Movement Bottlenecks and Optimization.mp4 | 12 MB | ||
| 16. AI Data Pipeline Design (ETL + Training + Inference).en_US.srt | 5.4 KB | ||
| 16. AI Data Pipeline Design (ETL + Training + Inference).mp4 | 11.5 MB | ||
| 17. Lab Design an End-to-End Data Pipeline for AI.html | 5.6 KB | ||
| 17. Lab3.pdf | 249.5 KB | ||
| 5 - Module 4 AI Cluster Orchestration and Scalability | |||
| 18. Kubernetes for GPU-Orchestrated AI Workloads.en_US.srt | 4.2 KB | ||
| 18. Kubernetes for GPU-Orchestrated AI Workloads.mp4 | 9.5 MB | ||
| 19. Helm, Operators, and Cluster Autoscaling.en_US.srt | 3.8 KB | ||
| 19. Helm, Operators, and Cluster Autoscaling.mp4 | 8.8 MB | ||
| 20. Integrating Slurm, Kubeflow, and MLflow.en_US.srt | 4.8 KB | ||
| 20. Integrating Slurm, Kubeflow, and MLflow.mp4 | 10.8 MB | ||
| 21. Cluster Topologies (On-prem, Cloud, Hybrid).en_US.srt | 4.8 KB | ||
| 21. Cluster Topologies (On-prem, Cloud, Hybrid).mp4 | 10.3 MB | ||
| 22. Lab Deploy Multi-GPU Training Job on Kubernetes.html | 5.6 KB | ||
| 22. Lab4.pdf | 179.2 KB | ||
| 6 - Module 5 Performance Optimization & Monitoring | |||
| 23. Profiling GPU Workloads (Nsight, DLProf, nvtop).en_US.srt | 5.6 KB | ||
| 23. Profiling GPU Workloads (Nsight, DLProf, nvtop).mp4 | 11.5 MB | ||
| 24. GPU Metrics, Telemetry & Alerting Tools.en_US.srt | 5.6 KB | ||
| 24. GPU Metrics, Telemetry & Alerting Tools.mp4 | 11.8 MB | ||
| 25. TensorRT and Model Optimization.en_US.srt | 5.4 KB | ||
| 25. TensorRT and Model Optimization.mp4 | 11 MB | ||
| 26. Bottleneck Diagnosis and Tuning.en_US.srt | 5.5 KB | ||
| 26. Bottleneck Diagnosis and Tuning.mp4 | 11.9 MB | ||
| 27. Lab Optimize Inference Pipeline with TensorRT.html | 5.8 KB | ||
| 27. Lab5.pdf | 186.8 KB | ||
| 7 - Module 6 Security, Compliance, and Data Governance | |||
| 28. Securing GPU-Powered Workloads.en_US.srt | 5.5 KB | ||
| 28. Securing GPU-Powered Workloads.mp4 | 12 MB | ||
| 29. Encryption and Access Control (DPUs, DOCA).en_US.srt | 6.2 KB | ||
| 29. Encryption and Access Control (DPUs, DOCA).mp4 | 14.3 MB | ||
| 30. Role-Based Access Control (RBAC) for AI Clusters.en_US.srt | 6.3 KB | ||
| 30. Role-Based Access Control (RBAC) for AI Clusters.mp4 | 13.9 MB | ||
| 31. Regulatory Compliance GDPR, HIPAA, FedRAMP.en_US.srt | 6.5 KB | ||
| 31. Regulatory Compliance GDPR, HIPAA, FedRAMP.mp4 | 14.7 MB | ||
| 32. Lab Apply Security Policies in AI Infrastructure.html | 5.8 KB | ||
| 32. Lab6.pdf | 180.6 KB | ||
| 8 - Module 7 Edge AI Infrastructure and Integration | |||
| 33. Edge vs Cloud AI – Infrastructure Implications.en_US.srt | 4 KB | ||
| 33. Edge vs Cloud AI – Infrastructure Implications.mp4 | 9.2 MB | ||
| 34. NVIDIA Jetson and Orin for Edge AI.en_US.srt | 4.9 KB | ||
| 34. NVIDIA Jetson and Orin for Edge AI.mp4 | 10.8 MB | ||
| 35. Federated Learning and Distributed Inference.en_US.srt | 4.6 KB | ||
| 35. Federated Learning and Distributed Inference.mp4 | 10.4 MB | ||
| 36. Use Cases Smart Cities, Retail, Industrial IoT.en_US.srt | 4.1 KB | ||
| 36. Use Cases Smart Cities, Retail, Industrial IoT.mp4 | 8.8 MB | ||
| 37. Lab Deploy AI Model to Jetson Nano.html | 5.9 KB | ||
| 37. Module7_lab.pdf | 272 KB | ||
| 9 - Module 8 NGC, Triton Inference Server & Deployment | |||
| 38. Using NGC Catalog for Pretrained Models.en_US.srt | 7.3 KB | ||
| 38. Using NGC Catalog for Pretrained Models.mp4 | 15.4 MB | ||
| 39. Triton Inference Server – Overview and Architecture.en_US.srt | 8 KB | ||
| 39. Triton Inference Server – Overview and Architecture.mp4 | 15.9 MB | ||
| 40. Model Ensemble and Multi-Framework Serving.en_US.srt | 6.8 KB | ||
| 40. Model Ensemble and Multi-Framework Serving.mp4 | 14.3 MB | ||
| 41. Lab Deploy Triton with TensorFlow and ONNX Models.html | 5.7 KB | ||
| 41. Module8_Lab.pdf | 150.6 KB | ||
| 42. Serving at Scale – Load Balancing and HA Design.en_US.srt | 5.4 KB | ||
| 42. Serving at Scale – Load Balancing and HA Design.mp4 | 15.2 MB | ||
| 8. MIG (Multi-Instance GPU) Configuration.en_US.srt | 6 KB | ||
| 8. MIG (Multi-Instance GPU) Configuration.mp4 | 13.7 MB | ||
| 9. GPU Sharing and Isolation Techniques.en_US.srt | 5.7 KB | ||
| 9. GPU Sharing and Isolation Techniques.mp4 | 12.6 MB | ||
| 3. Introduction to AI Infrastructure Design.en_US.srt | 6.6 KB | ||
| 3. Introduction to AI Infrastructure Design.mp4 | 14 MB | ||
| 4. Role of GPUs in AI Workloads.en_US.srt | 5.2 KB | ||
| 4. Role of GPUs in AI Workloads.mp4 | 11.1 MB | ||
| 5. CPU vs GPU vs DPU Architectures.en_US.srt | 4.8 KB | ||
| 5. CPU vs GPU vs DPU Architectures.mp4 | 10.1 MB | ||
| 6. GPU Acceleration for AI ML Pipelines.en_US.srt | 5.2 KB | ||
| 6. GPU Acceleration for AI ML Pipelines.mp4 | 11.2 MB | ||
| 7. NVIDIA Ecosystem Overview (CUDA, Triton, NGC).en_US.srt | 5.2 KB | ||
| 7. NVIDIA Ecosystem Overview (CUDA, Triton, NGC).mp4 | 12 MB | ||
| 2. Mock Test 60 Questions.html | 49 KB | ||
| 48. Exam Blueprint and Common Pitfalls.en_US.srt | 4.3 KB | ||
| 48. Exam Blueprint and Common Pitfalls.mp4 | 9.8 MB | ||
| 49. 10.2FlashCards.pdf | 90.2 KB | ||
| 49. Flashcards Concepts, Commands, Tools.html | 5.8 KB | ||
| 50. Capstone Project End-to-End AI Infrastructure Design.html | 5.9 KB | ||
| 50. CapstoneProject.pdf | 94.3 KB | ||
| 51. Certification Pathways and Next Steps.en_US.srt | 4.5 KB | ||
| 51. Certification Pathways and Next Steps.mp4 | 9.1 MB | ||
| 43. Case Study Building an AI Supercomputer.en_US.srt | 6.1 KB | ||
| 43. Case Study Building an AI Supercomputer.mp4 | 18.1 MB | ||
| 44. Case Study Multi-Tenant AI Infrastructure for Healthcare.en_US.srt | 6.5 KB | ||
| 44. Case Study Multi-Tenant AI Infrastructure for Healthcare.mp4 | 22.9 MB | ||
| 45. End-to-End Workflow Data → Train → Deploy → Monitor.en_US.srt | 5.6 KB | ||
| 45. End-to-End Workflow Data → Train → Deploy → Monitor.mp4 | 13.3 MB | ||
| 46. Lab Design and Present a Scalable AI Infrastructure.html | 5.6 KB | ||
| 46. Module9_Lab.pdf | 121.6 KB | ||
| 47. Peer Review.html | 5.4 KB | ||
| 47. PeerReview.pdf | 44.7 KB | ||
| 2. Introduction to NVIDIA-Certified Professional AI Infrastructure (NCP-AII).en_US.srt | 3.6 KB | ||
| 2. Introduction to NVIDIA-Certified Professional AI Infrastructure (NCP-AII).mp4 | 9.3 MB |
SoAI-Certified Professional: AI Infrastructure (NCP-AII)
https://WebToolTip.com
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.
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