Managing many datasets, data-sources and transformations for machine learning is complex and costly. Poorly cleaned data, data issues, bugs in transformations, data drift and training serving skew all leads to increased model development time and worse model performance. Here, feature store is well positioned to solve many of the problems since it provides a centralised way to transform and access data for training and serving time and helps defines a standardised pipeline for ingestion of data and querying of data.
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