Integrating Feature Stores in ML architecture.

Introduction:

According to the World Economic Forum, at the beginning of 2020, the number of bytes in the digital world is 40 times the number of stars available in the observable Universe. That sure is massive, but the real question we have to ask is, can we make sense of this abundant data? The data has to be cleaned, transformed and engineered, and stored to make sense of it.

During this process, a lot of care must be provided to …

architecture data-centralization data quality feature feature-store machine learning ml mlops