Implementing Cisco Data Center AI Infrastructure Course

Course Code: DCAI

Duration: 5 days

Version: 1.0

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Course Overview

The Implementing Cisco Data Center AI Infrastructure (DCAI) course is designed to equip professionals with the skills to support, secure, and optimize AI workloads within modern data center environments.

This comprehensive program delves into the unique characteristics of AI/ML applications, their influence on infrastructure design, and best practices for automated provisioning. Participants will gain in-depth knowledge of security considerations for AI deployments and master day-2 operations, including monitoring and advanced troubleshooting techniques such as log correlation and telemetry analysis. Through hands-on experience, including practical application with tools like Splunk, learners will be prepared to efficiently monitor, diagnose, and resolve issues in AI/ML-enabled data centers, ensuring optimal uptime and performance for critical organizational workloads.

This training prepares you for the 300-640 DCAI v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfy the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification.

This training is worth 38 Continuing Education (CE) Credits.

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Target Audience

Anyone looking to:

Acquire comprehensive skills to support, secure, and optimize AI workloads within modern data center environments

Understand the design, implementation, and advanced troubleshooting of AI infrastructure, including network challenges and specialized hardware

Gain in-depth knowledge of AI/ML concepts, generative AI, and their practical application in network management and automation

Apply hands-on techniques for monitoring, diagnosing, and resolving issues, leveraging tools like Splunk and utilizing AI for enhanced productivity in network operations

Prepare for the 300-640 DCAI v1.0 exam

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Course Objectives

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 compute hardware required to run AI/ML solutions
• Understand existing intelligence and 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

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Course Content

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

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

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)

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

Al Toolset-Jupyter Notebook

• AI Toolset-Jupyter Notebook

Al Infrastructure

• Traditional AI Infrastructure
• Modern AI Infrastructure

Al Workloads Placement and Interoperability

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

Al Policies

• Data Sovereignty
• Compliance, Governance, and Regulations

Al Sustainability

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

Al Infrastructure Design

• Project Description
• Your Role

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

Al 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

Al 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

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 Al

• Data Processing Workflow Overview
• Data Processing Workflow Phases

AI/ML Workload Data Performance

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

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

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

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

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

Setting Up Al Cluster

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

Deploy and Use Open Source GPT Models for RAG

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

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

Troubleshooting Al Infrastructure

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

Troubleshoot Common Issues in AI/ML Fabric

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

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Course Prerequisites

Attendees should have the following skills and knowledge:

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

Recommended prerequisites:

• DCCOR - Implementing and Operating Cisco Data Center Core Technologies
• DCNDE - Cisco Data Center Nexus Dashboard Essentials
• DCNX - Implementing Cisco NX-OS Switches and Fabrics in the Data Center

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Test Certification

Recommended as preparation for the following exam:

• 300-640 - DCAI - Implementing Cisco Data Center AI Infrastructure

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