Feature engineering is one of the most challenging aspects of the machine learning (ML) lifecycle and a phase where the most amount of time is spent—data scientists and ML engineers spend 60–70% of their time on feature engineering. AWS introduced Amazon SageMaker Feature Store during AWS re:Invent 2020, which is a purpose-built, fully managed, centralized store for features and associated metadata. Features are signals extracted from data to train ML models. The advantage of Feature Store is that the feature engineering logic is authored one time, and the features generated are stored on a central platform. The central store of features can be used for training and inference and be reused across different data engineering teams.
Features in a feature store are stored in a collection called feature group. A feature group is analogous to a database table schema where columns represent features and rows represent individual records. Feature groups

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