NCA-AIIO: NCA - AI Infrastructure and Operations
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Rating
4.6
Students
133
Duration
04:36:38 h
$16.49
$14.99
Curriculum for NCA-AIIO Certification Video Course
Introduction
1 Lectures
Time 00:01:28
Certification Details
2 Lectures
Time 00:04:28
Module 1 - Fundamentals
5 Lectures
Time 00:15:35
Module 2 - Inside an AI centric Data Center
13 Lectures
Time 01:00:05
Module 3 - Technology Stack
41 Lectures
Time 02:31:24
Module 4 - AI Workflows
10 Lectures
Time 00:43:38
| Name of Video | Time |
|---|---|
 1. Introduction |
1:28 |
| Name of Video | Time |
|---|---|
1. Certification Details |
2:31 |
2. Topics Covered in Certification |
1:57 |
| Name of Video | Time |
|---|---|
1. Drivers of AI evolution |
2:19 |
2. AI use cases across industries |
1:59 |
3. AI, ML, DL, Gen AI |
3:14 |
4. Analogy for AI, ML, DL, Gen AI |
3:14 |
5. Transformer Model |
4:49 |
| Name of Video | Time |
|---|---|
1. Inside an AI centric Data Center |
4:02 |
2. Power Usage Effectiveness (PUE) |
5:01 |
3. The Compute Power |
4:23 |
4. CPU and GPU |
7:09 |
5. CPU vs. GPU - Architectural difference |
2:46 |
6. Beyond Moore’s law |
2:33 |
7. Data Processing Unit (DPU) |
6:43 |
8. Network inside an AI centric Data Center |
4:48 |
9. Network fabric |
4:41 |
10. Ethernet vs. InfiniBand |
7:04 |
11. Converged Ethernet (CE) |
2:27 |
12. Storage inside an AI centric Data Center |
4:19 |
13. Cloud vs. On-Prem |
4:09 |
| Name of Video | Time |
|---|---|
1. Powering AI GPU Innovation |
2:35 |
2. Technology Stack |
3:10 |
3. Layer 1 - Physical Layer |
3:51 |
4. GPU on a Graphic Card |
1:56 |
5. DGX Platform |
2:54 |
6. DGX SuperPOD |
1:53 |
7. ConnectX |
1:48 |
8. BlueField DPUs |
2:30 |
9. Reference Architectures |
1:32 |
10. Understanding GPU Cores |
4:57 |
11. Comparing GPU Cores |
4:17 |
12. DGX Platform - Timeline |
4:46 |
13. DGX Platform - Deployment Options |
3:35 |
14. DGX A100 vs H100 |
4:01 |
15. Layer 2: Data Movement and I/O Acceleration |
0:56 |
16. NVLink |
8:01 |
17. InfiniBand |
2:03 |
18. InfiniBand vs. Ethernet |
1:41 |
19. DMA and RDMA |
6:07 |
20. GPUDirect RDMA |
2:22 |
21. GPUDirect Storage |
1:42 |
22. Quick Comparison |
1:31 |
23. Layer 3: OS, Driver and Virtualization |
2:16 |
24. GPU Drivers |
4:36 |
25. GPU Virtualization |
5:07 |
26. vGPU vs. MIG - Part 1 |
7:47 |
27. vGPU vs. MIG - Part 2 |
10:55 |
28. Layer 4: Core Libraries |
6:42 |
29. Compute Unified Device Architecture (CUDA) |
3:10 |
30. Installing CUDA |
2:07 |
31. Collective Communications Library (NCCL) |
3:39 |
32. NVLink, NVSwitch, PCIe, RDMA vs. NCCL |
3:42 |
33. Layer 5: Monitoring and Management |
2:21 |
34. GPU Identification Utility |
4:21 |
35. Data Center GPU Manager (DCGM) |
7:26 |
36. Base Command Manager |
5:31 |
37. Which one to use? |
2:02 |
38. Layer 6: Applications & Vertical Solutions |
3:46 |
39. Summary |
2:25 |
40. AI Enterprise |
3:01 |
41. AI Factory |
2:22 |
| Name of Video | Time |
|---|---|
1. AI Workflows |
5:20 |
2. ML Frameworks |
3:08 |
3. The differentiator |
1:40 |
4. Model Training vs. Model Inference |
7:45 |
5. Job Scheduling vs. Container Orchestration |
6:12 |
6. Slurm vs Kubernetes |
4:55 |
7. Integration |
2:28 |
8. ML Ops - Analogy |
4:15 |
9. Why ML Ops? |
3:41 |
10. Tools supporting ML Ops |
4:14 |
NVIDIA NCA-AIIO Exam Dumps, Practice Test Questions
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NVIDIA NCA-AIIO Training Course
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