MLOps-Testing

As ML models are increasingly deployed in high-stakes environments, ensuring their robustness, correctness, and fairness is essential. Our methodology tackles the critical challenge of quality assurance and testing for AI-enabled systems, particularly those based on machine learning (ML).

The IML4E testing methodology outlines systematic testing within MLOps processes to enhance quality and efficiency through targeted testing strategies. By combining classical software engineering practices with data science activities, it ensures reliability and quality across all development, integration, and operational phases. The methodology supports testing of both classical software and ML components, emphasizing performance, reliability, security, and compliance. It specifically addresses automated testing challenges for ML systems and categorizes metrics by learning type (i.e. supervised, unsupervised, reinforcement) and assembles established testing methods such as requirements-based and risk-based testing with advanced techniques, including differential testing, metamorphic testing, attack simulations, and AB testing.

The methodology provides best practices unmatched by current standards, offering detailed metrics, methods, and acceptance criteria necessary for rigorous ML system quality assurance and identifies all relevant artifacts per lifecycle phase, along with appropriate acceptance criteria and testing objectives.

In summary, it offers efficient yet rigorous testing approaches covering the entire ML system lifecycle and has been published as part of ETSI TR 103910.