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Create Batch Features: ELT/ETL systems like Spark and SQL are used to transform data in the batch store.
Create Stream Features: Stream features are created from streaming services such as Kafka or Kinesis, and can be pushed directly into Feast via the Push API.
Feast Apply: The user (or CI) publishes versioned controlled feature definitions using feast apply. This CLI command updates infrastructure and persists definitions in the object store registry.
Feast Materialize: The user (or scheduler) executes feast materialize which loads features from the offline store into the online store.
Model Training: A model training pipeline is launched. It uses the Feast Python SDK to retrieve a training dataset that can be used for training models.
Get Historical Features: Feast exports a point-in-time correct training dataset based on the list of features and entity dataframe provided by the model training pipeline.
Deploy Model: The trained model binary (and list of features) are deployed into a model serving system. This step is not executed by Feast.
Prediction: A backend system makes a request for a prediction from the model serving service.
Get Online Features: The model serving service makes a request to the Feast Online Serving service for online features using a Feast SDK.
A complete Feast deployment contains the following components:
Feast Registry: An object store (GCS, S3) based registry used to persist feature definitions that are registered with the feature store. Systems can discover feature data by interacting with the registry through the Feast SDK.
Feast Python SDK/CLI: The primary user facing SDK. Used to:
Manage version controlled feature definitions.
Materialize (load) feature values into the online store.
Build and retrieve training datasets from the offline store.
Retrieve online features.
Stream Processor: The Stream Processor can be used to ingest feature data from streams and write it into the online or offline stores. Currently, there's an experimental Spark processor that's able to consume data from Kafka.
Batch Materialization Engine: The Batch Materialization Engine component launches a process which loads data into the online store from the offline store. By default, Feast uses a local in-process engine implementation to materialize data. However, additional infrastructure can be used for a more scalable materialization process.
Online Store: The online store is a database that stores only the latest feature values for each entity. The online store is either populated through materialization jobs or through stream ingestion.
Offline Store: The offline store persists batch data that has been ingested into Feast. This data is used for producing training datasets. For feature retrieval and materialization, Feast does not manage the offline store directly, but runs queries against it. However, offline stores can be configured to support writes if Feast configures logging functionality of served features.
Java and Go Clients are also available for online feature retrieval.
In general, we recommend using Python for your Feature Store microservice.
As mentioned in the document, precomputing features is the recommended optimal path to ensure low latency performance. Reducing feature serving to a lightweight database lookup is the ideal pattern, which means the marginal overhead of Python should be tolerable. Because of this we believe the pros of Python outweigh the costs, as reimplementing feature logic is undesirable.
