What Makes ‘Good’ Data? A View from the Front Lines of AI

Over the past decade, the focus in AI has shifted from merely accumulating large quantities of data to understanding and curating it for better model performance. This shift emphasizes the need for data observability, which is crucial for identifying issues like redundancy, imbalance, and mislabeling that can lead to model failures. The article discusses the transition from open source code to open source data, highlighting how open datasets have advanced the field by making data more accessible and reproducible. The development of tools like FiftyOne by Voxel51 aims to provide machine learning engineers with the ability to inspect and analyze datasets, thereby enhancing the understanding of data and its impact on model performance. This approach advocates for data-centric AI practices, focusing on the quality of data rather than the quantity, to build models that are robust, fair, and reliable.