CISCO - DCAIE

Déploiement et exploitation de solutions d'IA sur infrastructure Cisco

Formation créée le 27/05/2026.
Version du programme : 1

Type de formation

Présentiel

Durée de formation

28 heures (4 jours)
Cette formation est gratuite.
S'inscrire
Cette formation est gratuite.
S'inscrire
Cette formation est gratuite.
S'inscrire
Besoin d’adapter cette formation à vos besoins ? N’hésitez pas à nous contacter afin d’obtenir un devis sur mesure ! Nous contacter

CISCO - DCAIE

Déploiement et exploitation de solutions d'IA sur infrastructure Cisco


The AI Solutions on Cisco Infrastructure Essentials (DCAIE) training covers the essentials of deploying, migrating, and operating AI solutions on Cisco data center infrastructure. You'll be introduced to key AI workloads and elements, as well as foundational architecture, design, and security practices critical to successful delivery and maintenance of AI solutions on Cisco infrastructure.

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 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

Profil des bénéficiaires

Pour qui
  • Network Designers
  • Network 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. This is an essentials training that progresses from beginner to intermediate content. Familiarity with Cisco data center networking and computing solutions is a plus but not a requirement. 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 Mastery - Jupyter Notebook
AI Infrastructure
AI Workload Placements 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
Congestive 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

Modalités de certification

Résultats attendus à l'issue de la formation
  • This training, along with the Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) training, prepares you for the Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and fulfill the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification.
Détails sur la certification
  • Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) v1.0 is a 90-minute exam associated with the Cisco Certified Specialist - Data Center AI Infrastructure certification and satisfies the concentration exam requirement for the CCNP® Data Center certification. This exam tests your knowledge and skills related to design, implementation, monitoring, and troubleshooting of AI infrastructure, including: Network Compute Storage Orchestration solutions