Read Feature Data

Reading feature data from Tecton enables you to use engineered features in your machine learning applications and pipelines. This overview provides context on these use cases, outlines the methods available for reading features in each scenario, and points you to relevant documentation with implementation details and examples.

Testing

During feature development, you can interactively test new feature definitions in your notebook environment to ensure their accuracy. Then, to prevent future regressions, you can define unit tests. See Testing Features for details on interactive and unit testing for Feature Views.

Training

To generate training data from your Tecton feature store, you can read historical feature data using the Feature Services API via the get_historical_features() method. When calling this method, provide a "spine" DataFrame containing the keys and timestamps for the samples you want to include, and Tecton returns a DataFrame with the feature values joined on. These values are point-in-time correct, meaning no future data is inadvertently included.

See Constructing Training Data for more details.

Inference

For online inference, you have a few options:

• Use the Tecton HTTP API to fetch single feature vectors at low latency.
• Use the Java Client Library, a wrapper for the HTTP API that handles best practices.
• Use the Python Client Library, a wrapper for the HTTP API that handles best practices.
• Subscribe your application to Feature View Output Streams to receive feature updates asynchronously.
• For offline batch inference, read historical features like you would for training using get_historical_features().