AI Solutions on Cisco Infrastructure Essentials (DCAIE)

After completing this course you should be able to:

• Describe key concepts in artificial intelligence, focusing on traditional AI, machine learning, and deep learning techniques and their applications
• Describe generative AI, its challenges, and future trends, while examining the nuances between traditional and modern AI methodologies
• Explain how AI enhances network management and security through intelligent automation, predictive analytics, and anomaly detection
• Describe the key concepts, architecture, and basic management principles of AI-ML clusters, as well as describe the process of acquiring, fine-tuning, optimizing and using pre-trained ML models
• Use the capabilities of Jupyter Lab and Generative AI to automate network operations, write Python code, and leverage AI models for enhanced productivity
• Describe the essential components and considerations for setting up robust AI infrastructure
• Evaluate and implement effective workload placement strategies and ensure interoperability within AI systems
• Explore compliance standards, policies, and governance frameworks relevant to AI systems
• Describe sustainable AI infrastructure practices, focusing on environmental and economic sustainability
• Guide AI infrastructure decisions to optimize efficiency and cost
• Describe key network challenges from the perspective of AI/ML application requirements
• Describe the role of optical and copper technologies in enabling AI/ML data center workloads
• Describe network connectivity models and network designs
• Describe important Layer 2 and Layer 3 protocols for AI and fog computing for Distributed AI processing
• Migrate AI workloads to dedicated AI network
• Explain the mechanisms and operations of RDMA and RoCE protocols
• Understand the architecture and features of high-performance Ethernet fabrics
• Explain the network mechanisms and QoS tools needed for building high-performance, lossless RoCE networks
• Describe ECN and PFC mechanisms, introduce Cisco Nexus Dashboard Insights for congestion monitoring, explore how different stages of AI/ML applications impact data center infrastructure, and vice versa
• Introduce the basic steps, challenges, and techniques regarding the data preparation process
• Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows
• Describe the importance of AI-specific hardware in reducing training times and supporting the advanced processing requirements of AI tasks
• Understand the computer hardware required to run AI/ML solutions
• Understand existing AI/ML solutions
• Describe virtual infrastructure options and their considerations when deploying
• Explain data storage strategies, storage protocols, and software-defined storage
• Use NDFC to configure a fabric optimized for AI/ML workloads
• Use locally hosted GPT models with RAG for network engineering tasks

Fundamentals of AI

• 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

Generative AI

• 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
• Generative AI vs. Traditional AI Data Requirements
• Future Trends in AI
• AI Language Models
• LLMs vs. SLMs

AI 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)

AI-ML Clusters and Models

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

AI Toolset Mastery - Jupyter Notebook

• AI Toolset-Jupyter Notebook

AI Infrastructure

• Traditional AI Infrastructure
• Modern AI Infrastructure
• Cisco Nexus HyperFabric AI Clusters

AI Workloads Placement and Interoperability

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

AI Policies

• Data Sovereignty
• Compliance, Governance, and Regulations

AI Sustainability

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

AI Infrastructure Design

• Project Description
• Your Role
• AI Workload Type
• Cloud vs. On-Prem
• The Choice of Network
• Choice of Platform and Sustainability
• Power Considerations

Key Network Challenges and Requirements for AI Workloads

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

AI Transport

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

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

AI Network

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

Architecture Migration to AI/ML Network

• Project Description
• Your Role
• Starting Small
• Going Beyond One Server
• Traffic Considerations

Application-Level Protocols

• RDMA Fundamentals
• RDMA Architecture
• RDMA Operations
• RDMA over Converged Ethernet

High-Throughput Converged Fabrics

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

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

Congestion Visibility

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

Data Preparation for AI

• Data Processing Workflow Overview
• Data Processing Workflow Phases

AI/ML Workload Data Performance

• AI/ML Workload Data Performance

AI-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

Compute Resources

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

Compute Resources Solutions

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

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

Storage Resources

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

Setting Up AI Cluster

• Setting Up AI Cluster

Deploy and Use Open Source GPT Models for RAG

• Deploy and Use Open Source GPTModels for RAG

Labs

• Discovery Lab 1: AI Toolset—Jupyter Not
• Discovery Lab 2: AI/ML Workload Data Performance
• Discovery Lab 3: Setting Up AI Cluster
• Discovery Lab 4: Deploy and Use Open Source GPT Models for RAG

Not available. Please contact.

Attendees should have an understanding of:

• Cisco UCS compute architecture and operations
• Cisco Nexus switch portfolio and features
• Data Center core technologies

This course is for anyone invloved in the implementation, maintenance and troubleshooting of a Cisco Data Center