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Demystifying AI and ML Infrastructure for Network Engineers

Tech Field Day via YouTube

Overview

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Learn how to build and manage GPU clusters for AI workloads in this technical presentation from Cisco at AI Field Day 5. Explore the challenges and solutions of setting up GPU clusters, including inter-GPU networking optimization using Cisco Nexus 9000 Series switches. Discover how to reduce cluster setup time from weeks to hours using validated solutions, and understand critical network designs like "Rails Optimized" and "Fly" for efficient GPU communication. Master concepts of collective communication protocols, dynamic load balancing, and static pinning for optimal data flow between GPUs. Gain insights into creating lossless networks using priority-based flow control and leveraging Nexus Dashboard for monitoring and anomaly detection. Follow along as a machine learning engineer demonstrates building a generative AI application using on-premises GPU infrastructure, showing how to process billions of tokens efficiently while maintaining data security. See real-world applications of AI/ML infrastructure in network engineering through practical examples of real-time insights and anomaly detection.

Syllabus

Demystifying Artificial Intelligence and Machine Learning Infrastructure for a Network Engineer

Taught by

Tech Field Day

Reviews

5.0 rating, based on 2 Class Central reviews

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  • Muhammad Awais Afnan
    A key strength of the course was its practical relevance. Instead of remaining purely theoretical, it covered real-world applications and design principles, which immediately contextualized the learning. Topics such as GPU clusters, data flow, and o…
  • Vinay Kumar Reddy Pisati
    Demystifying AI and ML Infrastructure for Network Engineers” by Tech Field Day is an excellent session that bridges the gap between traditional networking and modern AI/ML-driven infrastructure. It clearly explains how AI workloads differ from standard IT systems, highlighting the importance of GPUs, data pipelines, and high-performance networking fabrics. The presenters do a great job simplifying complex concepts for network engineers, showing how their existing skills apply to AI environments. The discussion on practical deployment, scalability, and integration with modern data centers makes it a must-watch for anyone preparing for the AI-driven networking future.

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