RELIABILITY ENGINEERING FOR MODERN DATA AND AI SYSTEMS

Data platforms and machine learning systems now sit on the critical path of decision making in most large organizations, yet the discipline used to keep them dependable lags far behind the discipline used for conventional software services. Site reliability engineering (SRE) gives service teams a shared vocabulary of service level indicators, service level objectives, error budgets, and blameless postmortems. Data and AI systems, however, fail in ways that those tools were not designed to catch: a pipeline can succeed while producing wrong numbers, and a model can serve predictions with low latency while its accuracy silently decays. This paper adapts reliability engineering to data and AI systems. It proposes a failure taxonomy that separates availability failures from correctness, freshness, and behavioral failures; defines a catalogue of data and model service level indicators; and describes an operating model in which data contracts, automated validation, drift monitoring, staged rollout, and incident practice are combined into a single reliability loop. The contribution is conceptual and practice oriented: it consolidates published research and reported practitioner experience into a structure that engineering teams can adopt incrementally without first building a large platform. The paper closes with limitations and with the measurement work that remains open.