Comparing model-centric and data-centric approaches to determine the efficiency of data-centric AI

In current machine learning approaches, data is crucial, yet it is often overlooked and mishandled in artificial intelligence (AI). As a result, many hours are wasted fine-tuning a model based on faulty data. Hence, there exists a new trend in AI, which is data-centric AI. We hypothesized that data-centric AI would improve the performance of a machine learning model. To test this hypothesis, three models (two model-centric approaches and one data-centric approach) were used. The model-centric approaches included basic data cleaning techniques and focused on the model, while the data-centric approach featured advanced data preparation techniques and basic model-training. We found that the data-centric approach gave a higher accuracy than the model-centric approaches. The model-centric approaches achieved 91% and 90% accuracy, respectively, whereas the data-centric approach achieved 97% accuracy.