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
t, the returned value is
the latest stored value before time
t. For example, if the
Vertex AI Feature Store stores the location information of a car at
110, the location at time
100 is used for requests at all
105). 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 ingestion so that you can do a bulk ingestion of values into a featurestore. For example, your computed source data might live in locations such as BigQuery or Cloud Storage. You can then ingest data from those sources into a featurestore so that feature values can be served in a uniform format from the central featurestore.
For more information, see Batch ingesting feature values.
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 that you can feed to a machine learning or batch prediction request.
Vertex AI Feature Store keeps feature values up to the data retention limit. This limit is based on the timestamp associated with the feature values, not when the values were imported. Vertex AI Feature Store schedules to delete values with timestamps that exceed the limit.
Online and offline storage
Vertex AI Feature Store uses two storage methods labeled as online
storage and offline storage, which are
priced differently. Online storage keeps
just the latest timestamp values of your features to efficiently handle online
serving request. When you run an ingestion job, you can control if data is
written to the online store by using the API. For example, when you run backfill
jobs, you can disable writes to the online store. For more information, see the
disableOnlineServing flag in the API
Vertex AI Feature Store uses offline storage to permanently store data until the data reaches the retention limit or until you delete the data. You can control offline storage costs by managing how much data you keep.
You can view how much online and offline storage you are using by using the Cloud Console. View your featurestore's Total offline storage and Total online storage monitoring metrics to see your usage.
Online serving nodes
Each featurestore instance has one or more online serving nodes. These nodes provide the compute resources that are used to serve feature values for low-latency online serving. The number of online serving nodes that you require is directly proportional on two factors. One factor is the number online serving requests (queries per second) that the featurestore receives, and the other factor is the number of ingestion jobs that write to online storage. You can check your featurestore's queries per second and the number of online serving nodes by viewing the Queries per second and Node count metrics in the console. For information about your ingestion jobs, you can view them by using the Cloud Console.
- Learn about setting up your project for Vertex AI Feature Store.
- Learn about source data requirements.