Data-centric AI is a growing concept that emphasizes the importance of high-quality data in the development and deployment of machine learning models. Large and diverse datasets are essential in training machine learning models that can handle real-world scenarios and produce reliable predictions, while high-quality data is critical in minimizing bias and ensuring ethical considerations are taken into account. Researchers and practitioners are using techniques such as data cleaning, data augmentation, and data synthesis to improve data quality and quantity. The availability of good data has been a vital factor in the success of AI, and continued efforts to enhance data quality and quantity will be crucial in advancing and deploying AI applicants.
