A Comparative Study of Data-centric vs Model-centric Approaches in Machine Learning

Recent trends in machine learning emphasize not only which models we build, but what data we use. In the traditional model-centric paradigm, researchers focus on refining algorithms and architectures (e.g. deeper networks or new optimizers) on a fixed dataset. In contrast, data-centric AI emphasizes improving the dataset itself (through label cleaning, augmentation, or curating examples) while keeping the model constant. We review these paradigms using illustrative cases: image classification on CIFAR-10 and sentiment analysis on the IMDB movie-review dataset. For example, Bhatt et al. find that a data-centric pipeline (cleaning labels and augmenting images) yields about 3% higher CIFAR-10 accuracy than purely tuning the model. Similarly, studies in NLP show that improving text data (balanced classes, noise removal) and using advanced embeddings (word2vec, BERT) both raise performance.