Kick-Starting Your Journey in Data-Centric Machine Learning
The data-centric machine learning (ML) approach has been created in response to the limitations of the model-centric paradigm. Although the data-centric perspective opens up incredible opportunities for the application of ML across new and existing domains, it doesn’t mean it’s easy to implement.
The appeal of the model-centric approach is its relative simplicity. Its dominance in the field isn’t necessarily because it’s superior, but rather because it’s more straightforward. It focuses primarily on refining models, tweaking algorithms, and enhancing computational power. However, this approach often neglects a fundamental aspect of ML – the quality and relevance of the data feeding these models.
The data-centric approach, on the other hand, prioritizes improving data quality over perfecting models. It recognizes that even the most sophisticated models can falter if they’...
