Data-centric development is a methodology that focuses on defining which projects or systems should be produced using available data. It differs from the more typically used model-centric approach in the following ways:
With a data-centric development strategy, using high-quality data—by identifying inconsistencies, labeling data correctly, and removing redundancies—significantly improves a model’s accuracy. Often, it results in better predictions or outcomes than repeatedly adjusting the model trained on a faulty dataset.
Adopting a data-centric approach for the machine learning lifecycle is challenging, which is why many enterprises shy away from such a strategy. However, there are two instances where it works best:
Below are best practices when shifting towards data-centric development:
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