The AI Solutions on Cisco Infrastructure Essentials (DCAIE) Learning Path 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 Learning Path, along with the Operate and Troubleshoot AI Solutions on Cisco Infrastructure (DCAIAOT) Learning Path, prepares you for the Implementing Cisco Data Center AI Infrastructure (300-640 DCAI) v1.0 exam. If passed, you earn the Cisco Certified Specialist - Data Center AI Infrastructure certification and fulfill the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Data Center certification.

Skills You'll Learn:

• Gain a general understanding of AI concepts, workloads, applications, use-cases, challenges, policies, governance, sustainability, and ethical considerations 
• Discover different network architectures, mechanisms, and protocols for building AI fabrics 
• Learn how to deploy, migrate, and operate AI solutions on Cisco data center infrastructure 
• Discover best practices for optimizing, automating, and maintaining AI technologies securely

Learning Path Objectives:

• AI Basics: Explore the world of AI, including its core concepts, applications, challenges, optimization, and deployment.  
• AI Infrastructure Requirements and Compliance: Examine the essential components, strategies, policies, and governance for building and managing AI infrastructure and workloads in modern data centers. 
• AI Network Architectures: Design AI-ready networks by examining the key network challenges and requirements to support AI workloads. 
• AI Data Considerations: Explore transport protocols and network fabrics to design congestion-free and lossless network infrastructures and data preparation to support AI workflows. 
• AI Hardware Resources: Examine the AI enabling hardware capable of delivering the needed computational and storage resources to support AI/ML clusters of different scales.