Twinkll Sisodia

When people think about AI infrastructure, most attention naturally gravitates toward model training. Training large models requires massive datasets, distributed compute, and specialized hardware accelerators. The engineering involved in orchestrating training jobs across clusters of graphics processing units (GPUs) or tensor processing units (TPUs) is significant, and it's often the most visible part of the AI lifecycle. Inference, by contrast, appears deceptively simple.

Skip to content Select a language Contact us What is Kubernetes monitoring? Monitoring is a way to understand the inner workings of a system. It provides an inside view of the infrastructure or application to help minimize downtime. It also makes it easier to manage a containerized infrastructure by regularly monitoring CPU, memory and storage usage.