The Model-Centric Approach vs. the Data-Centric Approach
Understand the key differences between model-centric and data-centric machine learning approaches. Learn why improving dataset quality is essential and how focusing on data refinement can overcome challenges faced by model-centric methods, leading to better model performance and reliability.
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.
Challenges in the model-centric approach
Labeling images in supervised ML is challenging due to the inherent ambiguity present in the data. Even professionals find it difficult to accurately label ...