Learning path
High Performance AI/ML Networking
Skill Level
Intermediate
Duration 1 hour 20 minutes
Updated Apr 21, 2024
About this learning path
Today, network engineers, especially in the data center space, must acquire AI/ML infrastructure skills and be able to discuss the required infrastructure upgrades and the reasoning for the upgrades with upper management. At WWT, we are committing $500 million to help our customers with AI/ML, and we have launched a new series of Learning Paths to help the reader navigate complex AI topics. By mastering these areas, data center network engineers can effectively contribute to successfully implementing and managing advanced AI and HPC infrastructure, aligning technological capabilities with business objectives while maintaining a robust and secure network environment.
Your instructors
Michael WitteWorld Wide TechnologyPrincipal Solutions Architect
Craig KemmererWorld Wide TechnologyTechnical Solutions Architect
Alex NadimiWorld Wide TechnologyTechnical Solutions Architect
Prerequisites
- A understanding of AI/ML terms and concepts from WWT AI Fundamentals Learning Path
- CCNA certification or a basic understanding of primary network concepts VLANs, IP address, and Gateways.
What you'll learn
- Why traffic patterns in a AI/ML GPU cluster is different then regular datacenter traffic
- The design challenges for back end high performance GPU networks
- Infiniband primer
- RMDA over Converged Ethernet (RoCE) primer
- Summary of OEM offerings
- Integration of white box switches and open networking operating systems
- Future of AI/ML infrastructure's