DCAIE | AI Solutions on Cisco Infrastructure Essentials
 
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.
 
Objectives
Have a basic knowledge of AI applications and use-cases.
Be aware of AI infrastructure compliance, governance, and sustainability.
Understand the AI workloads and the data center infrastructure requirements.
Understand different network architectures, mechanisms, and protocols for building AI fabrics.
Gain a broad knowledge of different compute and storage technologies used in building AI infrastructure.
Learn about different AI infrastructure provisioning automation options.
Understand different deployment options from security perspective.
Learn to properly maintain AI infrastructure, with focus on monitoring and troubleshooting.

How You'll Benefit
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.

Who Should Enroll
Customers, Partners, and Internal Solutions Engineers who are ready to embark on designing and deploying AI solutions on Cisco infrastructure.

Prerequisites
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.

Technology areas
Data Center
Artificial Intelligence (AI)

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 Considerations
Key Network Challenges and Requirements for AI Workloads
AI Transport
Connectivity Models
AI Network
Architecture Migration for AI/ML Networks
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
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste.