Operations | Monitoring | ITSM | DevOps | Cloud

You bought the GPUs to make AI training faster. So why are they sitting idle? When GPU utilization drops during a training run, the obvious answer is to blame the accelerator. Maybe the workload is too small. Maybe the GPU isn't powerful enough. Maybe it's time to add more hardware. But what if the GPU isn't the problem at all? A training workload is only as fast as the infrastructure feeding it.

Summarize with ChatGPT Claude The Hyperping MCP server now has 49 tools: 28 that read and 21 that write. An agent connected from Claude Code, Cursor, Codex or another MCP client could already manage monitors, publish a status page incident and schedule maintenance. It can now do most of the rest: declare an incident and page on-call, acknowledge and escalate it, correct what was posted on the status page, create and configure status pages, and reschedule, end or cancel maintenance.

Autonomous IT operations use AI, operational data, observability, and automation to enable IT environments to detect issues, understand their context, determine the appropriate response, and act with minimal human intervention. As businesses grow, IT environments rarely stay simple. More employees, endpoints, applications, and cloud services generate even more alerts, incidents, and operational work. The traditional model scales linearly: more environment means more manual effort.

AI database governance can get overlooked when teams rush to connect AI tools to production data. IBM’s 2025 research found that 97% of organizations that reported a breach involving an AI model or application lacked proper AI access controls. The risk is easy to see. Give an AI agent too much access and it can expose or alter data in seconds. Feed it poor-quality data and it may produce a confident but incorrect answer.

A practical session on SCOM Reporting and reporting alternatives.