What exactly is a Feature Store?

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Feature Stores address these issues by allowing data teams to:

Feature stores organize the data processing that drive machine learning models. To support model training and production inference, ML models have specific data access needs. The Feature Store acts as a bridge between your raw data and the model’s interfaces. Feature Stores enable data scientists to automate the processing of feature values, produce training datasets, and offer features online with production-grade service levels, thereby creating this abstraction.

What is the purpose of a Feature Store?

Feature Store Components

Because the Feature Store is a new concept, the precise definition is continually changing. The following are frequent features of a feature store:

What to consider when choosing a Feature Store

Users can now choose from a wide range of feature store products. AWS, Databricks, Google Cloud, Tecton, and Feast (open source) are just a few examples. Not all feature stores, however, are considered equivalent. When selecting an offering, a user should consider the following factors:

Originally published at https://aleksbasara.co on July 20, 2022.

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Reimagining the interaction between digital experiences and physical objects. Go Metaverse. Collecting GIFs and jpegs on @tezos

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Aleksandar Basara

Reimagining the interaction between digital experiences and physical objects. Go Metaverse. Collecting GIFs and jpegs on @tezos