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Reading Feature Data for Inference

This section covers how to read feature data from Tecton for model inference. There are a few main methods for reading feature data for inference:

This section provides an overview of each method, details on implementation, code samples, and links to relevant API references. Reading feature data for inference unlocks model predictions in production using features engineered and stored in Tecton.

Aggregations during online feature read

This section explains how aggregations are performed when reading feature data from Tecton.

During materialization, Tecton keeps track of the latest timestamp that has been written to the online store for each feature views in an internal status table.

For a batch materialization job, the status table is updated if the job completes, even if no new rows were written.

For a stream materialization job, the status table is updated strictly based on newer rows based and their timestamps.

When reading feature data from Tecton, all aggregations are performed relative to the latest timestamp written to the online store for the feature view, rather than relative to the current wall clock time. This means that retrieved feature values update at the same rate as newer stream values arrive (i.e. slowly-updating feature views have slowly-updating feature values).

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