The Model-Centric Approach vs. the Data-Centric Approach
Understand the fundamental difference between the data-centric and model-centric approaches to machine learning.
Data and algorithms are the building blocks of any ML and AI system.
The model-centric approach
The model-centric approach involves developing algorithms or techniques to enhance the performance of a ML model. This approach strongly emphasizes selecting the most suitable architecture or algorithm to yield the best possible performance for a given task or problem.
Moreover, this approach primarily focuses on optimizing the model itself rather than extensively manipulating or preprocessing the data. It aims to identify and implement the most effective algorithms, architectures, and techniques to produce the best results. The following figure demonstrates the model-centric approach.
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