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:
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Using the Tecton HTTP API: This is the recommended method for reading feature data for online inference. The HTTP API provides low latency reads of individual or batch sets of feature vectors. See Reading Online Features for Inference Using the HTTP API for details.
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Using the Java Client Library: This open-source library provides a convenient wrapper for the Tecton HTTP API. It handles best practices like request retries and response deserialization. See Reading Online Features for Inference using the Java Client for details.
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Using Feature Output Streams: This method allows your application to subscribe to the outputs of streaming feature pipelines. As new events arrive, feature values are written to the stream. This method is designed for asynchronous predictions triggered by new data. See Reading Online Features for Inference via a Feature Output Stream for details.
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Using the Python SDK: This method can be used to read either online or offline features in a notebook for testing purposes. However, the Python SDK is not suitable for production inference workloads. See Reading Online Features for Inference using the Python SDK (for Testing) or Reading Offline Features for Inference for details.
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.
