Driving performance optimization, fault detection and real-time orchestration of AI workloads in dynamic environments
Running AI training and inferencing workloads at scale for multiple AI models will place stringent and difficult-to-predict demands on AI infrastructure, including GPU and compute resources, storage capacity and network performance. Sub-optimal performance and reliability will increase costs while directly impacting enterprise AI applications that support critical business functions.
AI infrastructure operations teams will require new tools, technologies and techniques for monitoring and controlling AI workloads, detecting faults and performance anomalies in real-time, and automating operations in complex, highly-dynamic environments where human operators will be outpaced by speed at which conditions are constantly changing. Monitoring and control tools need to be linked directly to the agentic AI orchestration layer for managing different types of AI agents, which may be implemented using different AI models.
