Who Should Attend

Network Designers
Network Administrators
Storage Administrators
Network Engineers
Systems Engineers
Data Center Engineers
Consulting Systems Engineers

Course Objectives

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

Course Outline