AI Data Center
This course covers the complete AI data-center lifecycle, from compute, GPU architecture, networking, storage, and cluster design through NVIDIA infrastructure, Kubernetes, generative AI workloads, cooling and power, monitoring, security, automation, hybrid cloud, operations, disaster recovery, and hands-on deployment.
Curriculum Areas
- Module 1: AI Data Center Fundamentals
- Module 2: GPU & AI Compute Infrastructure
- Module 3: AI Networking
- Module 4: AI Storage
- Module 5: AI Cluster Architecture
- Module 6: NVIDIA AI Infrastructure
- Module 7: AI Software Stack
- Module 8: Generative AI Infrastructure
- Module 9: Data Center Cooling & Power
- Module 10: AI Data Center Monitoring
- Module 11: Security for AI Data Centers
- Module 12: AI Data Center Automation
- Module 13: Cloud & Hybrid AI Data Centers
- Module 14: AI Data Center Operations
- Module 15: Hands-On Projects
AI Data Center Curriculum
- What is an AI Data Center?
- Traditional Data Center vs AI Data Center
- AI/ML workload requirements
- CPU, GPU, TPU and AI accelerators
- AI infrastructure architecture
- AI Data Center components
- Compute, storage and networking fundamentals
- GPU architecture fundamentals
- NVIDIA GPU ecosystem
- GPU servers and GPU clusters
- GPU memory: HBM, VRAM
- GPU interconnects
- PCIe, NVLink and NVSwitch
- Multi-GPU systems
- GPU resource allocation
- GPU utilization and performance optimization
- AI Data Center networking fundamentals
- Ethernet vs InfiniBand
- High-speed networking
- 100G/200G/400G/800G networking concepts
- RDMA and RoCE
- Network topology for AI clusters
- Leaf-spine architecture
- Network load balancing
- Congestion management
- Network monitoring and troubleshooting
- AI storage architecture
- NAS vs SAN vs Object Storage
- High-performance storage
- NVMe and NVMe-oF
- Parallel file systems
- Data pipelines for AI
- Storage performance optimization
- Checkpoint storage
- Backup and disaster recovery
- AI cluster design
- GPU cluster architecture
- Compute nodes
- Management nodes
- Storage nodes
- Network architecture
- GPU scheduling
- Cluster provisioning
- High availability
- Scaling AI clusters
- NVIDIA AI platform overview
- NVIDIA DGX systems
- NVIDIA HGX architecture
- NVIDIA Networking
- NVIDIA CUDA fundamentals
- CUDA libraries
- NCCL
- NVIDIA GPU monitoring
- GPU cluster management
- NVIDIA AI Enterprise overview
- Linux for AI Data Centers
- Containers and Docker
- Kubernetes for AI workloads
- NVIDIA GPU Operator
- Kubernetes GPU scheduling
- Container orchestration
- AI/ML frameworks
- PyTorch and TensorFlow infrastructure
- Model serving
- Generative AI architecture
- LLM infrastructure
- Training vs inference
- LLM GPU requirements
- Distributed AI training
- Model parallelism
- Data parallelism
- Inference optimization
- LLM serving infrastructure
- AI power requirements
- Rack power management
- High-density GPU racks
- Air cooling
- Liquid cooling
- Direct-to-chip cooling
- Power Usage Effectiveness (PUE)
- Thermal monitoring
- Energy optimization
- Infrastructure monitoring
- GPU monitoring
- CPU and memory monitoring
- Network monitoring
- Storage monitoring
- Temperature and power monitoring
- Performance metrics
- Logs and alerts
- Capacity planning
- Troubleshooting methodology
- AI infrastructure security
- Zero Trust architecture
- GPU security
- Network security
- Container security
- Kubernetes security
- Identity and access management
- Data protection
- Secrets management
- AI workload isolation
- Infrastructure as Code
- Terraform fundamentals
- Ansible automation
- Python automation
- Kubernetes automation
- GPU provisioning
- Automated monitoring
- Automated incident response
- CI/CD for AI infrastructure
- AWS AI infrastructure
- Microsoft Azure AI infrastructure
- Google Cloud AI infrastructure
- Hybrid AI architecture
- On-premises vs cloud AI
- Cloud GPU instances
- Multi-cloud AI infrastructure
- AI workload migration
- Data Center deployment lifecycle
- Hardware provisioning
- GPU cluster operations
- Capacity management
- Performance tuning
- Fault diagnosis
- Hardware replacement
- Firmware management
- SLA and availability
- Disaster recovery
- Build an AI GPU server environment
- Configure NVIDIA GPU drivers
- Deploy CUDA
- Configure Docker with GPU support
- Deploy Kubernetes GPU cluster
- Install NVIDIA GPU Operator
- Configure GPU monitoring
- Deploy an AI/LLM workload
- Build an AI inference server
- Monitor GPU utilization and performance
- Troubleshoot GPU/network/storage issues