Why testing should be at the core of machine learning development.

Machine learning (ML) development often lacks the rigorous testing and release processes of traditional software engineering, leading to systems that perform well in controlled environments but fail in real-world situations, posing risks such as undetected pedestrians or flawed COVID diagnostics. While traditional software benefits from test-driven development and continuous integration, ML systems frequently adopt a "ship-to-test" approach, resulting in vulnerabilities only emerging during operation. Lakera aims to address these issues by integrating systematic testing into ML development, with their MLTest tool automatically identifying vulnerabilities before deployment, thereby enhancing the reliability and safety of AI products. By adopting software engineering best practices, ML teams can improve the quality and speed of AI product development, mitigating risks associated with insufficient testing.

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