DCAI - Implementing Cisco Data Center AI Infrastructure v1.0

Section 1: Fundamentals of Al

• Introduction to Artificial Intelligence
• Traditional AI
• Traditional AI Process Flow
• Traditional AI Challenges
• Modern Applications of Traditional AI
• Machine Learning vs. Deep Learning
• ML vs. DL Techniques and Methodologies
• ML vs. DL Applications and Use Cases

Section 2: Generative Al

• Generative AI
• Generative Adversarial Frameworks
• GenAI Use Cases
• Generative AI Inference Challenges
• GenAI Challenges and Limitations
• GenAI Bias and Fairness
• GenAI Resource Optimization
• Generative AI vs. Traditional AI
• Future Trends in AI
• AI Language Models
• LLMs vs. SLMs

Section 3: Al Use Cases

• Analytics
• Network Optimization
• Network Automation and Self-Healing Networks
• Capacity Planning and Forecasting
• Cybersecurity
• Predictive Risk Management
• Threat Detection
• Incident Response
• Collaboration and Communication
• Internet of Things (IoT)

Section 4: Al-ML Clusters and Models

• AI-ML Compute Clusters
• AI-ML Cluster Use Cases
• Custom AI Models-Process
• Custom AI Models-Tools
• Prebuilt Al Model Optimization
• Pre-Trained AI Models
• AI Model Parameters
• Service Placements - On-Premises vs. Cloud vs. Distributed

Section 5: Al Toolset-Jupyter Notebook

• AI Toolset-Jupyter Notebook

Section 6: Al Infrastructure

• Traditional AI Infrastructure
• Modern AI Infrastructure

Section 7: Al Workloads Placement and Interoperability

• Workload Mobility
• Multi-Cloud Implementation
• Vendor Lock-In Risks
• Vendor Lock-In Mitigation

Section 8: Al Policies

• Data Sovereignty
• Compliance, Governance, and Regulations

Section 9: Al Sustainability

• Green AI vs. Red AI
• Cost Optimization
• AI Accelerators
• Power and Cooling

Section 10: Al Infrastructure Design

• Project Description
• Your Role

Section 11: Key Network Challenges and Requirements for Al Workloads

• Bandwidth and Latency Considerations
• Scalability Considerations
• Redundancy and Resiliency Considerations
• Visibility
• Nonblocking Lossless Fabric
• Congestion Management Considerations

Section 12: Al Transporf

• Optical and Copper Cabling
• Organizing Data Center Cabling
• Ethernet Cables
• InfiniBand Cables
• Ethernet Connectivity
• InfiniBand Connectivity
• Hybrid Connectivity

Section 13: Connectivity Models

• Network Types: Isolated vs. Purpose-Built Network
• Network Architectures: Two-Tier vs. Three-Tier Hierarchical Model
• Networking Considerations: Single-Site vs. Multi-Site Network Architecture

Section 14: Al Network

• Layer 2 Protocols
• Layer 3 Protocols
• Scalability Considerations for Deploying AI Workloads
• Fog Computing for AI Distributed Processing

Section 15: Architecture Migration to AI/ML Network

• Project Description
• Your Role

Section 16: Application-Level Protocols

• RDMA Fundamentals
• RDMA Architecture
• RDMA Operations
• RDMA over Converged Ethernet > NEW title RoCE/RoCEv2
  Objectives:
• Understand the RDMA operations over Ethernet

Section 17: High-Throughput Converged Fabrics

• InfiniBand-to-Ethernet Transition
• Cisco Nexus 9000 Series Switches Portfolio

Section 18: Building Lossless Fabrics

• Traditional QoS Toolset
• Enhanced Transmission Selection
• Intelligent Buffer Management on Cisco Nexus 9000 Series Switches
• AFD with ETRAP
• Dynamic Packet Prioritization
• Data Center Bridging Exchange
• Lossless Ethernet Fabric Using RoCEv2
• Advanced Congestion Management with AFD

Section 19: Congestion Visibility

• Explicit Congestion Notification
• Priority Flow Control
• Congestion Visibility in AI/ML Cluster Networks Using Cisco Nexus Dashboard Insights
• Pipeline Considerations

Section 20: Data Preparation for Al

• Data Processing Workflow Overview
• Data Processing Workflow Phases

Section 21: AI/ML Workload Data Performance

• Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows

Section 22: Al-Enabling Hardware

• CPUs, GPUs, and DPUs
• GPU Overview
• NVIDIA GPUs for AI/ML
• Intel GPUs for AI/ML
• DPU Overview
• SmartNIC Overview
• Cisco Nexus SmartNIC Family
• NVIDIA BlueField SuperNIC

Section 23: Compute Resources

• Compute Hardware Overview
• Intel Xeon Scalable Processor Family Overview
• Cisco UCS C-Series Rack Servers
• Cisco UCS X-Series Modular System
• Mapping AI/ML Workloads to Cisco UCS Servers
• GPU Sharing
• Compute Resources Sharing
• Total Cost of Ownership
• AI/ML Clustering

Section 24: Compute Resource Solutions

• Cisco Hyperconverged Infrastructure Solutions Overview
• Cisco Hyperconverged Solution Components
• FlashStack Data Center
• Nutanix GPT-in-a-Box
• Run:ai on Cisco UCS

Section 25: Virtual Resources

• Virtual Infrastructure
• Device Virtualization
• Server Virtualization Defined
• Virtual Machine
• Hypervisor
• Container Engine
• Storage Virtualization
• Virtual Networks
• Virtual Infrastructure Deployment Options
• Hyperconverged Infrastructure
• HCI and Virtual Infrastructure Deployment

Section 26: Storage Resources

• Data Storage Strategy
• Fibre Channel and FCoE
• NVMe and NVMe over Fabrics
• Software-Defined Storage

Section 27: Setting Up Al Cluster

• Use NDFC to configure a fabric optimized for AI/ML workloads.

Section 28: Deploy and Use Open Source GPT Models for RAG

• Use locally-hosted GPT models with RAG for network engineering tasks.

Section 29: Al Infrastructure Operations and Monitoring

• The Need for AI Infrastructure Monitoring
• Monitoring Compute
• Monitoring Storage
• Monitoring the Runtime Layer
• Monitoring AI Fabrics
• The Need for Al Infrastructure Lifecycle Management
• Compute Lifecycle Upgrades
• Fabric Lifecycle Upgrades

Section 30: Troubleshooting Al Infrastructure

• Log Correlation for AI Applications
• Telemetry Analysis for AI Workloads
• Hands-On Telemetry for AI Workloads
• Timing Protocols

Section 31: Troubleshoot Common Issues in AI/ML Fabric

• Overview of Splunk Enterprise and Splunk Cloud
• Data Ingestion Methods
• Splunk Applications
• Basics of Splunk SPL

• Discovery 1: AI Toolset-Jupyter Notebook
• Design Practice Activity 1: AI Infrastructure Design
• Design Practice Activity 2: Architecture Migration to Al/ML Network
• Discovery 2: AI/ML Workload Data Performance
• Discovery 3: Setting Up AI Cluster
• Discovery 4: Deploy and Use Open Source GPT Models for RAG
• Discovery 5: Troubleshoot Common Issues in AI/ML Fabric