DCAIE

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

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

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

AI Toolset—Jupyter Notebook
AI/ML Workload Data Performance
Setting Up AI Cluster
Deploy and Use Open Source GPT Models for RAG