Integrating Quality Assurance into MLOps for Reliable ML Systems: Engineering Methods and Evidence

Machine learning (ML) applications in critical sectors such as healthcare and finance face significant reliability concerns despite advancements in automated development and deployment pipelines. Conventional software quality assurance methods, which focus on code correctness, are insufficient for dynamic ML systems that are susceptible to data drift, bias, and adversarial attacks. This study addresses the fragmented systematic of quality assurance (QA) in machine learning operations (MLOps) by proposing an integrated framework. This framework embeds lifecycle-wide QA modules spanning the data, model, and infrastructure layers into MLOps pipelines, coordinated via continuous integration and deployment (CI/CD). Improved reliability was demonstrated in a financial fraud detection case, as evidenced by fewer production incidents, rapid drift detection, increased fairness scores, and steady accuracy. Therefore, the case study validates that continuous and automated QA in MLOps significantly reduces operational risks and increases trust, thereby posing QA as an indispensable integrated service rather than a post-deployment afterthought for robust and ethical ML systems. We evaluate the framework on a financial-fraud workload over 90 days, contrasting a baseline pipeline without QA and a QA-integrated pipeline. The evaluation tracks fairness score (0–1), drift-detection latency, monthly production incident rate, accuracy stability, and data-validation error rate.