Data-Centric AI focuses on improving the quality and management of data rather than solely concentrating on the development of algorithms or models. It involves:
Data Quality: Ensuring that data is accurate, complete, and representative of the problem domain.
Data Annotation: Correctly labeling data to train machine learning models effectively.
Data Augmentation: Creating additional data samples to enhance model performance.
Data Management: Organizing data in a way that makes it easier to access and use.