Deploying a machine learning pipeline is a resource-demanding task that requires a combination of data and software engineering expertise. However, even with meticulous testing, the risk of encountering run-time errors during pipeline operation remains a significant concern. In this presentation, our focus lies in addressing the run-time errors caused by mismatches between the data to be processed and the code responsible for its processing. We present strategies for the early detection of these mismatches through static and dynamic analysis, leveraging techniques from JSON Schema reasoning. Specifically, we showcase our recent contributions to essential tasks such as schema validation, schema extraction, and checking schema containment. Furthermore, we provide an outlook on the challenges introduced by the latest drafts of JSON Schema. Lastly, we conclude with a discussion on application domains for our contributions, extending beyond the fortification of machine learning pipelines against run-time errors.