API
Contents
Credentials configuration
Different data sources require different credentials which users need to specify.
Starting the client
Explore the first steps on how to use the Feature Store client.
Default naming rules
Feature Store is configured to adhere to the restrictions on setting names for a workspace or a feature set as explained in this page.
Authentication
The Feature Store Python client authenticates through two entry points; platform discovery via login() and explicit OIDC via login_custom().
Permissions
Permissions determine the level of access that a user has to various components of the Feature Store. For example, depending on the level of permission granted, a user may be authorized to edit feature sets, while another user with limited view-only permission can only observe the feature set.
Workspaces API
A workspace groups related feature sets, jobs, drafts, and reviews together.
Schema API
A schema is extracted from a data source. The schema represents the features of the feature set.
Feature set API
Registering a feature set
Feature API
Using FeatureType Enum
Ingest API
Feature store ensures that data for each specific feature set does not contain duplicates. That means that only data which are unique to the feature set storage are ingested as part of the ingest operation. The rows that would lead to duplicates are skipped. Ingest can be run on instance of feature set representing any minor version. The data are always ingested on top of latest storage stage.
Ingest history API
Getting the ingestion history
Retrieve API
Retrieval API overview.
Jobs API
Listing jobs
Create new feature set version API
A feature set is a collection of features. Users can create a new version of an existing feature set for various reasons.
Asynchronous methods
Several methods in the Feature Store Client API have asynchronous variants.
Spark dependencies
Users can interact with Feature Store from a Spark session by adding several dependencies on the Spark Classpath. Supported Spark versions are 4.1.x.
Recommendation API
A Recommendation API can be used to suggest personalized recommendations based on the data stored in the feature sets. If users have two different feature sets, they can use a Recommendation API to find similarities between the features in those sets and recommend features that are similar in nature or data type.
Feature set schedule API
Users can schedule an ingestion job from Feature Store by using API available on the feature sets.
Feature set review API
Feature set review process involves the reviewer's acceptance. Depending on the system configuration, all feature sets or only sensitive ones may be subject to review.
Dashboard API
Dashboard provides a short summary about the usage of Feature store.
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