CISCO- DCAI

Implementing Cisco Data Center AI Infrastructure

Formation créée le 19/02/2026.
Version du programme : 1

Type de formation

Mixte

Durée de formation

35 heures (5 jours)

CISCO- DCAI

Implementing Cisco Data Center AI Infrastructure


The Implementing Cisco Data Center AI Infrastructure (DCAI) training 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.

Objectifs de la formation

  • 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

Profil des bénéficiaires

Pour qui
  • Network Designers
  • Network Administrators
  • Storage Administrators
  • Network Engineers
  • Systems Engineers
  • Data Center Engineers
  • Consulting Systems Engineers
  • Technical Solutions Architects
  • Cisco Integrators/Partners
  • Field Engineers
  • Server Administrators
  • Network Managers
  • Program Managers
  • Project Managers
Prérequis
  • There are no prerequisites for this training. However, the knowledge and skills you are recommended to have before attending this training are: Cisco UCS compute architecture and operations Cisco Nexus switch portfolio and features Data Center core technologies

Contenu de la formation

Fundamentals of AI
Generative AI
AI Use Cases
AI-ML Clusters and Models
AI Toolset—Jupyter Notebook
AI Infrastructure
AI Workloads Placement and Interoperability
AI Policies
AI Sustainability
AI Infrastructure Design
Key Network Challenges and Requirements for AI Workloads
AI Transport
Connectivity Models
AI Network
Architecture Migration to AI/ML Network
Application-Level Protocols
High-Throughput Converged Fabrics
Building Lossless Fabrics
Congestion Visibility
Data Preparation for AI
AI/ML Workload Data Performance
AI-Enabling Hardware
Compute Resources
Compute Resource Solutions
Virtual Resources
Storage Resources
Setting Up AI Cluster
Deploy and Use Open Source GPT Models for RAG
AI Infrastructure Operations and Monitoring
Troubleshooting AI Infrastructure
Troubleshoot Common Issues in AI/ML Fabric

Modalités de certification

Détails sur la certification
  • 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.

Capacité d'accueil

Entre 3 et 10 apprenants

Délai d'accès

12 semaines