By Peter Kisich,
Publication Date: 2025-11-20 02:45:00
As artificial intelligence (AI) moves from the lab to the enterprise, a critical reality is emerging: the infrastructure that powers it is fundamentally different. AI and machine learning (ML) workloads are not like traditional applications.
They demand:
- Immense computational power: Delivered by specialized accelerators like GPUs that can process parallel computations at a scale far beyond standard CPUs.
- Ultra-fast, low-latency networking: Required to connect large clusters of accelerators for distributed training, where any network bottleneck can bring a multi-million dollar AI investment to a standstill.
- Sophisticated orchestration: Needed to manage and schedule complex jobs across a pool of specialized hardware, ensuring maximum utilization and efficiency.
This has created an unprecedented demand for technical professionals who can design, deploy, and manage these highly specialized data center environments. To meet this demand, Cisco has introduced a new AI…
