In the context of artificial intelligence, many production failures originate not from the neural networks but from the deterministic infrastructure that supports these models, such as data pipelines, feature engineering, and post-processing components. The text emphasizes the importance of implementing rigorous unit-testing strategies to ensure the reliability of these components, thereby preventing data corruption, schema shifts, and configuration changes from affecting AI performance. It advocates for comprehensive testing approaches that include schema validation, feature engineering validation, and integration tests to detect potential failures early in the data processing stages. Additionally, it highlights the need for post-processing tests to enforce business rules and maintain output quality, along with the use of platforms like Galileo to monitor and evaluate AI performance in production environments. By focusing on these foundational aspects, AI teams can minimize outages and ensure that their models perform as expected in real-world applications.