Progressive Delivery for Models with Quality KPIs

Abstract

Progressive delivery, a technique that has been in use for a long time in software engineering, gradually gets essential applications in ML and AI model deployment scenarios, where the risks of performance drift, ethical lapses, or bias amplification require cautious and measurable release strategies. This article discusses the use of gradual delivery concepts in ML models, which are mainly controlled, incremental rollouts regulated by well-defined Quality Key Performance Indicators (KPIs). Progressive delivery allows for staged exposure thus, a model is not immediately deployed to full production, but it is exposed only to limited user segments or traffic percentages from the very beginning and the real-time validation metrics such as precision, latency, fairness, and stability are used to make the decisions on promotion or rollback. Before they may get a chance to interact with the model, progressive delivery principles ensure that ML-based changes are tested, verified, and monitored for quality on a smaller scale and with the possibility of rapid rollback. Accordingly, the paper offers a well-thought-out approach that mixes CI/CD principles with ML observability frameworks to build a smart, feedback-driven delivery pipeline. The paper presents a case study that shows how companies can put this framework into practice, managing the trade-off between innovation speed and performance assurance. The experimental evidence conveys that the suggested model deployment pipeline not only encourages implementation trust but also leads to the performance anomalies' early and quick recovery, eventually raising model robustness and business impact to higher levels. Ultimately, the paper conceptualizes progressive delivery as a core capability for the deployment of responsible AI through this synthesis of engineering rigor and statistical governance, thus enabling a smooth transition from experimentation to production at scale.