The following sections introduce the Vertex AI Feature Store data model and terminology that is used to describe Vertex AI Feature Store resources and components.
Vertex AI Feature Store data model
Vertex AI Feature Store uses a time series data model to store a
series of values for features. This model enables
Vertex AI Feature Store to maintain feature values as they change
over time. Vertex AI Feature Store organizes resources hierarchically
Featurestore -> EntityType -> Feature. You must create these
resources before you can ingest data into Vertex AI Feature Store.
As an example, assume that you have the following sample source data from a BigQuery table. This source data is about movies and their features.
Before you can ingest this data into Vertex AI Feature Store, you need to create a featurestore, which is a top-level container for all other resources. In the featurestore, create entity types that group and contain related features. You can then create features that map to features in your source data. The names of the entity type and features can mirror the column header names, but that is not required.
In this example, the
movie_id column header can map to an entity type
genre are features of the
movie entity type. The values in each column map to specific instances of an
entity type or features, which are called entities and feature values.
The timestamp column indicates when the feature values were generated. In the featurestore, the timestamps are an attribute of the feature values, not a separate resource type. If all feature values were generated at the same time, you are not required to have a timestamp column. You can specify the timestamp as part of your ingestion request.
A featurestore is the top-level container for entity types, features, and feature values. Typically, an organization creates one shared featurestore for feature ingestion, serving, and sharing across all teams in the organization. However, sometimes you might choose to create multiple featurestores within the same project to isolate environments. For example, you might have separate featurestores for experimentation, testing, and production.
An entity type is a collection of semantically related features. You define your
own entity types, based on the concepts that are relevant to your use case. For
example, a movie service might have the entity types
which group related features that correspond to movies or customers.
An entity is an instance of an entity type. For example,
movie_02 are entities of the entity type
movie. In a
featurestore each entity must have a unique ID and must be of type
A feature is a measurable property or attribute of an entity type. For example,
movie entity type has features such as
track various properties of movies. Features are associated with entity types.
Features must be distinct within a given entity type, but they don't need
to be globally unique. For example, if you use
title for two different entity
types, Vertex AI Feature Store interprets
title as two different
features. When reading feature values, you provide the feature and its entity
type as part of the request.
When you create a feature, you specify its value type such as
STRING. This value determines what value types
you can ingest for a particular feature. For more information about the
supported value types, see the
valueType in the
Vertex AI Feature Store captures feature values for a feature at a
specific point in time. In other words, you can have multiple values for a given
entity and feature. For example, the
movie_01 entity can have multiple feature
values for the
average_rating feature. The value can be
4.4 at one time and
4.8 at some later time. Vertex AI Feature Store associates a tuple
identifier with each feature value (
which Vertex AI Feature Store uses to look up values at serving time.
Vertex AI Feature Store stores discrete values even though time is
continuous. When you request a feature value at time
Vertex AI Feature Store returns the latest stored value at or before
t. For example, if the Vertex AI Feature Store stores the
location information of a car at times
110, the location at time
100 is used for requests at all times between
100 (inclusive) and
(exclusive). If you require higher resolution, you can, for example, infer the
location between values or increase the sampling rate of your data.
Feature ingestion is the process of importing feature values computed by your feature engineering jobs into a featurestore. Before you can ingest data, the corresponding entity type and features must be defined in the featurestore. Vertex AI Feature Store offers batch and streaming ingestion, letting you add feature values in bulk or in real time.
For example, you might have computed source data that live in locations such as BigQuery or Cloud Storage. You can batch ingest data from those sources into a central featurestore so that those feature values can be served in a uniform format. As your source data changes, you can use streaming ingestion to quickly get those changes into your featurestore. That way, you have the latest data available for online serving scenarios.
Feature serving is the process of exporting stored feature values for training or inference. Vertex AI Feature Store offers two methods for serving features: batch and online. Batch serving is for high throughput and serving large volumes of data for offline processing (like for model training or batch predictions). Online serving is for low-latency data retrieval of small batches of data for real-time processing (like for online predictions).
When you retrieve values from a featurestore, the service returns an entity view that contains the feature values that you requested. You can think of an entity view as a projection of the features and values that Vertex AI Feature Store returns from an online or batch serving request:
- For online serving requests, you can get all or a subset of features for a particular entity type.
- For batch serving requests, you can get all or a subset of features for one or more entity types. For example, if features are distributed across multiple entity types, you can retrieve them together in a single request, which joins those features together. You can then use the results to feed to a machine learning or batch prediction request.
Vertex AI Feature Store lets you export data from your featurestores so that you can backup and archive feature values. You can choose to export the latest feature values (snapshot) or a range of values (full export). For more information, see Export feature values.
- Learn about setting up your project for Vertex AI Feature Store.
- Learn about source data requirements.