What is data-centric AI, and 3 reasons to pay attention to it

Data-centric AI is gaining attention as a paradigm shift from the traditional model-centric approach in machine learning, emphasizing the importance of high-quality data over simply amassing large datasets. This shift, advocated by experts like Stanford professor Andrew Ng, argues that iteratively improving data quality while keeping models fixed can yield better results. The data-centric approach suggests enhancing model performance by targeting specific data slices, conducting error analysis, and refining data quality, which can be more effective than repeatedly changing modeling strategies. Despite the availability of commoditized model architectures through libraries like scikit-learn or TensorFlow, the distinct advantage in AI applications lies in the quality of the training data, which remains challenging to standardize. This evolving approach encourages practitioners to focus on constructing well-represented datasets, thereby improving model outcomes and addressing edge cases systematically.