Data-centric and model-centric machine learning – By Aditya Abeysinghe   Different approaches are used in machine learning to build AI (Artificial Intelligence) models. Two common methods used are the model-centric method and the data-centric method. The model-centric method focuses on improving the model and the data-centric method focuses on improving the data used for building the model. Both methods have benefits and drawbacks and both can be used in any model. Model-centric approach In the model-centric approach, the data used for the model is not changed. The model is changed to increase the accuracy and the performance. Different methods to improve the model are used like increasing the training cycles until overfitting, changing values of inputs in each training cycle etc. Most machine learning models are built using this method to improve the model as it is often easy to change the model when compared to changing data. Data-centric approach ...

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