ENGINEERING ROBUST AI PRODUCTS THROUGH CONTINUOUS QUALITY ASSURANCE: A FRAMEWORK FOR TESTING, MONITORING, AND VALIDATION OF ADAPTIVE LIVE LEARNING AI/ML SYSTEMS IN DYNAMIC PRODUCTION ENVIRONMENTS

Abstract

The rise of online learning, continuous data ingestion, and live machine learning systems in production environments introduce new challenges for evaluating testing methodologies. Unlike traditional ML workflows, these adaptive systems continuously absorb new data and evolve over time, rendering static testing methods and offline validation datasets inadequate for ensuring reliability, fairness, and robustness. Existing quality assurance practices largely emphasize pre-deployment validation and lack systematic, automated approaches for real-time quality assurance within continuously evolving ML pipelines.

This paper proposes a framework that is implemented as a modular prototype and evaluated using data streaming scenarios, such as real-time recommendation systems and fraud detection models. The research aims to demonstrate the effectiveness of continuous verification in reducing undetected model failures, improving system reliability, and shortening feedback-to-repair cycles. By embedding QA automation into MLOPs pipelines, this study addresses a critical gap in current testing methodologies for adaptive ML systems, contributing towards scalable, audit-ready and resilient AI deployments.