📄️ Credentials configuration
To be able to read data from different data sources, you need to pass
📄️ Starting the client
Once your Python environment is ready, run:
📄️ Default naming rules
Feature Store is configured to adhere to the following restrictions on
Feature Store CLI provides 3 forms of authentication:
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.
📄️ Projects API
📄️ Schema API
A schema is extracted from a [data
📄️ Feature set API
Registering a feature set
📄️ Feature API
📄️ Ingest API
Feature store ensures that data for each specific feature set does not
📄️ Ingest history API
Getting the ingestion history
📄️ Retrieve API
To retrieve the data, first run:
📄️ Jobs API
📄️ 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
📄️ Spark dependencies
If you want to interact with Feature Store from a Spark session, several
📄️ Recommendation API
A Recommendation API can be used to suggest personalized recommendations based on the data stored in the feature sets.
📄️ Feature set schedule API
You can schedule an ingestion job from Feature Store by using API
📄️ Feature view API
Creating a feature view
📄️ Feature set review API
The 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.