The hidden devops crisis that AI workloads are about to expose

"The problem is, that doesn't test what actually matters-whether the system as a whole can handle production workloads. This simple approach breaks down fast when AI workloads start generating massive volumes of data that need to be captured, processed, and fed back into models in real time. If data pipelines can't keep up, AI systems can't perform. Traditional observability approaches can't handle the volume and velocity of data that these systems now generate."

"The challenge is that many teams bolt on observability as an afterthought. They'll instrument production but leave lower environments relatively blind. This creates a painful dynamic where issues don't surface until staging or production, when they cost significantly more to fix. The solution is instrumenting at the lowest levels of the stack, even in developers' local environments. This adds tooling overhead up front, but it allows you to catch data schema mi"

Traditional component-level testing and simple monitoring fail to validate whether systems can handle production AI workloads and massive data flows. AI workloads generate high-volume, high-velocity data that must be captured, processed, and fed back into models in real time, and data pipelines that lag will degrade AI performance. Observability must extend beyond production into lower environments and developers' local setups to surface schema and data issues early. Teams need comprehensive internal platforms or "paved roads" that replicate production and enable dynamic data pipelines and immediate end-to-end verification. Resilience testing must run at every stack layer to ensure availability and handle failure scenarios without harming inference quality or business decisions.

Read at InfoWorld