DCAIE
Artificial Intelligence (AI)
AI Solutions on Cisco Infrastructure Essentials
32 horas
Nível: Profissional
Presencial (SP) e Online
Introdução
The AI Solutions on Cisco Infrastructure Essentials (DCAIE) training covers the essentials of deploying, migrating, and operating AI solutions on Cisco data center infrastructure.
Objetivo do curso
Describe key concepts in AI, machine learning, and deep learning
Describe generative AI, challenges, and future trends
Explain how AI enhances network management and security
Describe key concepts and architecture of AI-ML clusters
Use Jupyter Lab and Generative AI to automate network operations
Describe essential components for AI infrastructure setup
Evaluate workload placement strategies and AI system interoperability
Explore AI compliance standards and governance frameworks
Describe sustainable AI infrastructure practices
Describe key network challenges for AI/ML workloads
Describe role of optical and copper technologies in AI/ML data centers
Describe network connectivity models and designs
Describe Layer 2 and Layer 3 protocols for AI and fog computing
Explain RDMA and RoCE protocols
Understand high-performance Ethernet fabric architecture
Explain QoS tools for lossless RoCE networks
Describe ECN and PFC mechanisms
Describe AI-specific hardware and compute requirements
Use NDFC to configure a fabric optimized for AI/ML workloads
Describe generative AI, challenges, and future trends
Explain how AI enhances network management and security
Describe key concepts and architecture of AI-ML clusters
Use Jupyter Lab and Generative AI to automate network operations
Describe essential components for AI infrastructure setup
Evaluate workload placement strategies and AI system interoperability
Explore AI compliance standards and governance frameworks
Describe sustainable AI infrastructure practices
Describe key network challenges for AI/ML workloads
Describe role of optical and copper technologies in AI/ML data centers
Describe network connectivity models and designs
Describe Layer 2 and Layer 3 protocols for AI and fog computing
Explain RDMA and RoCE protocols
Understand high-performance Ethernet fabric architecture
Explain QoS tools for lossless RoCE networks
Describe ECN and PFC mechanisms
Describe AI-specific hardware and compute requirements
Use NDFC to configure a fabric optimized for AI/ML workloads
Público-alvo
Network Designers, Network Administrators, Storage Administrators, Network Engineers, Systems Engineers, Data Center Engineers, Technical Solutions Architects, Field Engineers
Pré-requisitos
No formal prerequisites. Recommended: familiarity with Cisco UCS, Nexus switch portfolio, and Data Center core technologies.
Conteúdo programático
Course Outline
Fundamentals of AI and Generative AI
AI Use Cases and AI-ML Clusters
AI Toolset (Jupyter Notebook)
AI Infrastructure
AI Workload Placements and Interoperability
AI Policies and Sustainability
AI Infrastructure Design
Key Network Challenges for AI Workloads
AI Transport and Connectivity Models
AI Network Architecture Migration
Application-Level Protocols (RDMA, RoCE)
High Throughput Converged and Lossless Fabrics
Congestive Visibility and Data Performance
AI-Enabling Hardware and Compute Resources
Storage Resources
Setting Up AI Cluster
Deploy and Use Open Source GPT Models for RAG
Lab Outline
AI Toolset - Jupyter Notebook
AI/ML Workload Data Performance
Setting Up AI Cluster
Deploy and Use Open Source GPT Models for RAG
Fundamentals of AI and Generative AI
AI Use Cases and AI-ML Clusters
AI Toolset (Jupyter Notebook)
AI Infrastructure
AI Workload Placements and Interoperability
AI Policies and Sustainability
AI Infrastructure Design
Key Network Challenges for AI Workloads
AI Transport and Connectivity Models
AI Network Architecture Migration
Application-Level Protocols (RDMA, RoCE)
High Throughput Converged and Lossless Fabrics
Congestive Visibility and Data Performance
AI-Enabling Hardware and Compute Resources
Storage Resources
Setting Up AI Cluster
Deploy and Use Open Source GPT Models for RAG
Lab Outline
AI Toolset - Jupyter Notebook
AI/ML Workload Data Performance
Setting Up AI Cluster
Deploy and Use Open Source GPT Models for RAG
Próximas turmas
Não há turmas abertas no momento. Entre em contato para verificar disponibilidade ou agendar turma fechada.
Solicitar Inscrição / Cotação Consultar via WhatsApp