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~Get Your Files Here !
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1 - Introduction to NVIDIA-Certified Professional AI Infrastructure (NCP-AII)
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10 - Module 9 Real-World Projects and Enterprise Workflows
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11 - Module 10 Final Capstone + Certification Prep
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2 - Module 1 Foundations of AI Infrastructure
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3. Introduction to AI Infrastructure Design.en_US.srt
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6.6 KB
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4. Role of GPUs in AI Workloads.en_US.srt
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5.2 KB
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5. CPU vs GPU vs DPU Architectures.en_US.srt
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4.8 KB
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6. GPU Acceleration for AI ML Pipelines.en_US.srt
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5.2 KB
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7. NVIDIA Ecosystem Overview (CUDA, Triton, NGC).en_US.srt
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5.2 KB
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3 - Module 2 GPU Resource Management and Virtualization
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10. Virtual GPUs (vGPU) Setup and Use Cases.en_US.srt
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5.9 KB
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11. GPU Workload Scheduling with Kubernetes.en_US.srt
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5.9 KB
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8. MIG (Multi-Instance GPU) Configuration.en_US.srt
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6 KB
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9. GPU Sharing and Isolation Techniques.en_US.srt
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5.7 KB
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4 - Module 3 Storage, Networking, and Data Pipelines for AI
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13. Storage Architectures for AI Workloads (local, shared, object).en_US.srt
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5.3 KB
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14. High-Speed Networking NVLink, Infiniband, RDMA.en_US.srt
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5.7 KB
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15. Data Movement Bottlenecks and Optimization.en_US.srt
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5.6 KB
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16. AI Data Pipeline Design (ETL + Training + Inference).en_US.srt
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5.4 KB
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5 - Module 4 AI Cluster Orchestration and Scalability
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18. Kubernetes for GPU-Orchestrated AI Workloads.en_US.srt
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4.2 KB
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19. Helm, Operators, and Cluster Autoscaling.en_US.srt
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3.8 KB
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20. Integrating Slurm, Kubeflow, and MLflow.en_US.srt
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4.8 KB
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21. Cluster Topologies (On-prem, Cloud, Hybrid).en_US.srt
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4.8 KB
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6 - Module 5 Performance Optimization & Monitoring
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23. Profiling GPU Workloads (Nsight, DLProf, nvtop).en_US.srt
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5.6 KB
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24. GPU Metrics, Telemetry & Alerting Tools.en_US.srt
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5.6 KB
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25. TensorRT and Model Optimization.en_US.srt
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5.4 KB
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