Data-centric AI: Building Stronger Models through Better Data

Data-centric AI is an approach that leverages domain expertise to create better machine learning models by prioritizing the quality and veracity of the data itself. It emphasizes treating data with care, similar to how one would code, and proposes techniques and frameworks to ensure high-quality data for AI projects. This approach places control over model quality in the hands of domain experts, allowing them to leverage their expertise for better results. Data-centric AI addresses AI's most pressing problems, including large datasets, data staleness, and annotation complexity, by providing remedies such as data labeling, data analysis, and data augmentation methods. By focusing on improving data quality, teams can reduce model decay, improve model performance, and increase the sustainability of their AI projects.