Feature store as a central feature repository
A large percentage of the time spent on any machine learning problem is on data cleansing and data wrangling to ensure we build our models on clean and meaningful data. Feature engineering is another critical process of the machine learning process where data scientists spend a huge chunk of their time curating machine learning features, which happens to be a complex and time-consuming process. It appears counter-intuitive to have to create features again and again for each new machine learning problem.
Typically, feature engineering takes place on already existing historic data, and new features are perfectly reusable in different machine learning problems. In fact, data scientists spend a good amount of time searching for the right features for the problem at hand. So, it would be tremendously beneficial to have a centralized repository of features that is also searchable and has metadata to identify features. This central repository...
