The text discusses the limitations of supervised machine learning and the importance of ensuring that training data is accurate. It highlights the issue of label errors in datasets, even those from real-world applications, which can lead to flawed models being deployed. To address this, the open-source cleanlab library provides a tool to identify label issues in datasets. The text then demonstrates how to use cleanlab on the MNIST dataset, which has been cited over 40,000 times, to find label issues and visualize specific examples that warrant closer inspection. The findings of cleanlab are used to demonstrate how to use the library to improve the accuracy of machine learning models by identifying and correcting label errors in datasets.