{"id":530,"date":"2023-04-13T06:36:54","date_gmt":"2023-04-13T06:36:54","guid":{"rendered":"http:\/\/geoiqfrontendblogsprodwordpress-env.eba-dpx723ys.ap-south-1.elasticbeanstalk.com\/index.php\/2023\/04\/13\/feature-store\/"},"modified":"2025-01-15T14:35:21","modified_gmt":"2025-01-15T09:05:21","slug":"feature-store-a-data-scientists-perspective","status":"publish","type":"post","link":"http:\/\/geoiq.ai\/blog\/feature-store-a-data-scientists-perspective\/","title":{"rendered":"Feature Store – A Data Scientist\u2019s Perspective"},"content":{"rendered":"

We have heard a lot about feature stores and how they are transforming the MLops space but all from the data engineering viewpoint. For a data scientist, the toolkit is rapidly expanding. Does it make sense for them to adopt one more?<\/p>\n

In the development stage of a machine learning project, data scientists do large amounts of feature engineering to find the features that lead to the highest prediction accuracy. Once that process is complete, they usually hand off the project to an engineering colleague who will put those feature engineering pipelines into production.<\/p>\n

If you\u2019re a data scientist, you don\u2019t want to be concerned about how the data becomes available or how it is computed. You know which features you want, and you want those features to be available for the model to make live predictions.<\/p>\n

Engineers, on the other hand, need to re-implement those data pipelines in a production environment, which quickly becomes very complex as soon as there\u2019s real-time or near-real-time data involved. To power operational ML applications, these pipelines need to run continuously, can\u2019t break, need to be extremely fast, and need to scale with the business.<\/p>\n

That\u2019s why there is a pressing need for data scientists to add feature stores to their toolkits.<\/p>\n

First, let’s see what is a feature store?<\/p>\n

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