(DCAIE) AI Solutions on Cisco Infrastructure Essentials
Master data center AI solutions with Cisco AI training. Gain skills in deployment, design & security—start DCAIE certification today!
Duration
Version
1.0
CE Credits
34
The Cisco Continuing Education Program enables you to renew your certifications while exploring new learning paths and enhancing your skillset. It's a convenient way to stay certified without retaking exams. Click here to learn more.
CLCs
35
Cisco Learning Credits (CLCs) are prepaid training vouchers that can be used as investment into your knowledge. You can attend NIL Learning’s trainings and pay with CLCs. Click here to learn more.
By default, our instructors deliver the courses remotely in real time in English. On-premise delivery and other languages available on request.
Dates and Prices
Cisco Learning Credits (CLCs) are prepaid training vouchers that can be used as investment into your knowledge. You can attend NIL Learning’s trainings and pay with CLCs. Click to learn more.
Course Overview
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. This training also earns 34 Continuing Education (CE) credits toward recertification.
This training will help you:
- Gain the knowledge you need to deploy, migrate, and operate AI solutions on Cisco data center infrastructure
- Qualify for professional-level job data center roles
- Earn 34 CE credits toward recertification
Welcome to Your Path to Mastery!
Hey! Ready to learn and grow? I'm here to help you every step of the way!
Welcome aboard! This course is tailored to empower you with the knowledge and skills you need. We'll make learning engaging and fun, turning your challenges into achievements. Let's get started on your amazing journey!
Goran Martić
Lead Instructor
Objectives
Gain expert IT skills with NIL Learning: Benefit from our blend of theory and practical experience. Enhance your growth with insights into current and future tech trends. Choose us for cutting-edge Cisco training and more, taught by field-proven experts to maximize your training investment.
- 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
Who should enroll?
- 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
Course benefits:
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Learn from the best in Cisco technologies
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Course Outline
- 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
Lab Outline
- AI Toolset—Jupyter Notebook
- AI/ML Workload Data Performance
- Setting Up AI Cluster
- Deploy and Use Open Source GPT Models for RAG
Prerequisite Knowledge
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
These skills can be found in the following Cisco Learning Offerings:
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Excellent instructor
Excellent instructor. Well organized. Presentation was clear and informative. Information provided was well thought out. I was very impressed with the instructor.
Positive attitude
Loved that every day, the instructor came with a positive attitude and was wiling to answer all of our questions.
Very good technical knowledge
The instructor was well prepared and had very good technical knowledge. He managed to inform us in an understandable way.