Operations | Monitoring | ITSM | DevOps | Cloud

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

I wanted to share a story of a recent engagement with a law firm to highlight the strategic importance of compliance in today’s legal sector. It started with a single email. A mid-sized law firm received a regulator’s request for evidence following a client complaint. The issue wasn’t malpractice; it was a missed filing deadline caused by a system slowdown. The firm had no audit trail to prove the delay was technical, not procedural.

In early October, Red Hat disclosed a breach of a GitLab system used by its Consulting division. Threat actors claim to have exfiltrated hundreds of gigabytes of project data — and while investigations are still underway, reports suggest consulting engagement artifacts may have been impacted. For the organizations involved, the concern isn’t limited to reputational damage.