Galileo, a data quality platform, aims to help NLP teams improve ML data quality by identifying and fixing common data errors. The most prominent types of data errors include mislabeled samples, class overlap, and imbalances in the dataset, which can degrade model performance. Galileo helps mitigate these errors by providing techniques for finding problematic classes, detecting class or metadata column imbalance, and reducing imbalances through downsampling and data augmentation. Additionally, it detects data drift, which occurs when real-world data "drifts" away from the training data, causing the model's predictions to become inaccurate. Galileo empowers users to find and fix data errors in minutes without worrying about technical details, providing a solution for the time-consuming process of finding data errors.