category
AI Solutions on Cisco Infrastructure Essentials – eLearning
Type
Skill Level
Available dates
Learning Path
Virtual
Duration
1 Day
LEARNING PATH
SKILL LEVEL
DURATION
AVAILABLE DATES
Choose date
R14 900,00
Price excluding VAT
Introduction
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.
Audience profile
| · Network Designers | · Technical Solutions Architects |
| · Network Administrators | · Cisco Integrators/Partners |
| · Storage Administrators | · Field Engineers |
| · Network Engineers | · Server Administrators |
| · Systems Engineers | · Network Managers |
| · Data Center Engineers | · Program Managers |
| · Consulting Systems Engineers | · Project Managers |
Pre-requisites
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
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 content
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Associated certifications and exam
No associated exam.
Cisco Overview
Torque IT is one South Africa’s larger and more experienced providers of Authorized Cisco training. We run more courses more often, than any other training provider in South Africa and we guarantee that you will receive the most up to date and relevant technical course information available when you attend Cisco training courses at Torque IT.
Our standard and customized Cisco training courses are hands-on. When you attend training at Torque IT, you will implement the concepts that you learn using current Cisco equipment or Cisco Learning Labs (CLL), in real-world scenarios, to prepare you for real networking environments and the associated Cisco Certification examinations. Our authorized Cisco training and associated certification solutions empower you to design, sell, implement, troubleshoot and maintain Cisco implementations of any size.
The above serves to illustrate our commitment to providing you with high quality skills development, enablement, training and certification solutions that demonstrate exceptional depth, breadth, and expertise across Routing & Switching, Network Security, Cyber Security, Wireless LAN, Industrial (IoT), Unified Communications, Cloud, Data Center Unified Fabric, Unified Computing Systems, Service Provider, Network Programmability, Software Defined Networking and IP NGN technologies.