CI/CD, infrastructure-as-code, observability, and operational practices adapted to AI workloads. Includes GPU capacity management, prompt and model versioning,
DevOps for AI systems extends infrastructure craft to a new workload class: GPU scheduling, model artifact pipelines, eval-gated releases, and observability that tracks quality alongside uptime, keeping AI estates shippable, debuggable, and affordable.
Strong convergence play: platform engineers who treat models, prompts, and GPUs as first-class workloads are the backbone of every scaling AI organization.
New artifacts (models, prompts), new hardware economics (GPUs), and a new failure dimension: quality regressions that ship while dashboards stay green. Eval-gated delivery is the signature addition.
Yes: GPU scheduling, inference autoscaling, and job orchestration largely run on it. K8s fluency plus AI workload patterns is a premium combination.