Quality metrics serve as a fundamental tool for evaluating and refining machine learning datasets and models by providing a structured way to index, slice, and analyze data. They are versatile functions that can be applied to data points, labels, or model predictions, enabling tasks such as data sorting, comparison, outlier detection, and performance evaluation. In machine learning projects, different types of quality metrics can be used: data quality metrics analyze raw data without labels, label quality metrics focus on label information to identify errors or assess annotator performance, and model quality metrics use model predictions to guide decisions on labeling priorities. Custom quality metrics can be developed for specific project needs using Encord Active, a platform that facilitates the definition, execution, and visualization of these metrics, allowing users to maximize the value of their data and models.