The use of machine learning (ML) allows us to automate and scale the decision-making processes. The key to this automation is the development of ML models that generalize training data toward unseen data. Such models can become extremely versatile and powerful, which makes democratization of artificial intelligence (AI) possible, that is, providing ML to non-ML experts such as software engineers or domain experts. Typically, automated ML (AutoML) is being referred to as a key step toward it. However, from our perspective, we believe that democratization of the verification process of ML systems is a larger and even more crucial challenge to achieve the democratization of AI. Currently, the process of ensuring that an ML model works as intended is unstructured. It is largely based on experience and domain knowledge that cannot be automated. The current approaches such as cross-validation or explainable AI are not enough to overcome the real challenges and are discussed extensively in this article. Arguing toward structured verification approaches, we discuss a set of guidelines to verify models, code, and data in each step of the ML lifecycle. These guidelines can help to reliably measure and select an optimal solution, besides minimizing the risk of bugs and undesired behavior in edge-cases.

中文翻译：

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工业环境中机器学习模型的结构化验证

机器学习 (ML) 的使用使我们能够自动化和扩展决策过程。这种自动化的关键是开发机器学习模型，将训练数据推广到看不见的数据。此类模型可以变得极其通用和强大，这使得人工智能 (AI) 的民主化成为可能，即向软件工程师或领域专家等非 ML 专家提供 ML。通常，自动化机器学习 (AutoML) 被称为实现这一目标的关键一步。然而，从我们的角度来看，我们认为机器学习系统验证过程的民主化是实现人工智能民主化的一个更大、甚至更关键的挑战。目前，确保机器学习模型按预期工作的过程是非结构化的。它很大程度上基于无法自动化的经验和领域知识。当前的方法（例如交叉验证或可解释的人工智能）不足以克服真正的挑战，本文将进行广泛讨论。在争论结构化验证方法时，我们讨论了一套在机器学习生命周期的每个步骤中验证模型、代码和数据的指南。这些指南可以帮助可靠地衡量和选择最佳解决方案，同时最大限度地减少边缘情况下出现错误和不良行为的风险。