Here is a summary of the provided text in one paragraph: Quality metrics are functions that assign values to individual data points, labels, or model predictions in a dataset, enabling informed actions to be taken during the active learning cycle. Data quality metrics capture properties of raw images or video frames without labels, while label quality metrics focus on the accuracy and consistency of annotations. Model quality metrics take into account the model's predictions, helping to identify areas for improvement and inform acquisition functions. With Encord Active and Index, users can define, execute, and utilize quality metrics to optimize data curation and model training processes, tailoring them to specific project requirements to achieve higher accuracy and reliability in machine learning projects.