Structured Verification of Machine Learning Models in Industrial Settings

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.