Continuous Testing for AI Applications

In the era of artificial intelligence, software testing has evolved from a finite phase in development to an ongoing, dynamic process of monitoring. Unlike traditional deterministic systems, AI-driven applications operate probabilistically, introducing variability and uncertainty in outputs even with consistent inputs. This paradigm shift requires a rethinking of testing strategies, moving towards continuous monitoring to ensure performance, fairness, and reliability in production environments. This session will explore how QA teams can integrate AI-specific methods, anomaly detection, and real-time feedback loops into their testing frameworks, enabling them to address AI’s unique challenges. Attendees will leave with actionable strategies for designing robust testing pipelines that align with the iterative and probabilistic nature of modern AI systems.