Skip to content

AI workloads generate cost at a velocity that outpaces manual review — by the time a bill surfaces a problem, the GPU hours are already gone. In this session, Pratyush Mishra, Distinguished Engineer at Capital One, shares how to govern both foundation models and custom training workloads through policy-driven controls that cover idle resource retirement, model tier enforcement, and upfront guardrails — with concrete examples.