CA-FMLOps: Evaluating Edge Filtering and Carbon-Aware Scheduling in Sustainable MLOps

Artificial intelligence can support climate mitigation, but the computing systems used to develop and operate it can also intensify energy demand, data movement, and greenhouse-gas emissions. This paper presents and evaluates CA-FMLOps—Carbon-Aware Frugal Machine Learning Operations—as a design-science framework for climate-technology systems that must operate under constrained energy, connectivity, compute, and financial conditions. The framework combines four controls: edge-first inference, event-triggered data transmission, carbon-aware scheduling of deferrable cloud workloads, and lifecycle measurement using SCI-aligned accounting. A 50-run Monte Carlo benchmark with 2,000 observations per run reports a 98.352% simulated operational-carbon reduction and a 99.985% simulated deadline-reliability rate for the full hybrid arm relative to the cloud-first baseline, while also exposing a 33.332% recall limitation.