3 Strategies for Making Your ML Testing Mission-Critical Now

Testing machine learning (ML) systems is essential, particularly in mission-critical contexts, where reliability and compliance are crucial. While companies like Tesla have advanced testing frameworks, smaller teams can also adopt effective ML testing strategies. These include specifying an operational domain to establish system reliability, stress-testing to evaluate robustness using techniques like fuzz testing and metamorphic relations, and employing regression sets to ensure iterative performance improvements. By implementing these strategies, smaller teams can achieve high returns on investment in developing reliable ML systems. Lakera's MLTest offers a tool to identify performance vulnerabilities in computer vision systems, enhancing reliability for development teams.