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Featurestore(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.
Attributes |
|
---|---|
Name | Description |
name |
str
Output only. Name of the Featurestore. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
|
create_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Timestamp when this Featurestore was created. |
update_time |
google.protobuf.timestamp_pb2.Timestamp
Output only. Timestamp when this Featurestore was last updated. |
etag |
str
Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens. |
labels |
MutableMapping[str, str]
Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. |
online_serving_config |
google.cloud.aiplatform_v1beta1.types.Featurestore.OnlineServingConfig
Optional. Config for online storage resources. The field should not co-exist with the field of OnlineStoreReplicationConfig . If both of it and
OnlineStoreReplicationConfig are unset, the feature store
will not have an online store and cannot be used for online
serving.
|
state |
google.cloud.aiplatform_v1beta1.types.Featurestore.State
Output only. State of the featurestore. |
online_storage_ttl_days |
int
Optional. TTL in days for feature values that will be stored in online serving storage. The Feature Store online storage periodically removes obsolete feature values older than online_storage_ttl_days since the feature generation
time. Note that online_storage_ttl_days should be less
than or equal to offline_storage_ttl_days for each
EntityType under a featurestore. If not set, default to 4000
days
|
encryption_spec |
google.cloud.aiplatform_v1beta1.types.EncryptionSpec
Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key. |
Classes
LabelsEntry
LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)
The abstract base class for a message.
Parameters | |
---|---|
Name | Description |
kwargs |
dict
Keys and values corresponding to the fields of the message. |
mapping |
Union[dict,
A dictionary or message to be used to determine the values for this message. |
ignore_unknown_fields |
Optional(bool)
If True, do not raise errors for unknown fields. Only applied if |
OnlineServingConfig
OnlineServingConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)
OnlineServingConfig specifies the details for provisioning online serving resources.
State
State(value)
Possible states a featurestore can have.
Values:
STATE_UNSPECIFIED (0):
Default value. This value is unused.
STABLE (1):
State when the featurestore configuration is
not being updated and the fields reflect the
current configuration of the featurestore. The
featurestore is usable in this state.
UPDATING (2):
The state of the featurestore configuration when it is being
updated. During an update, the fields reflect either the
original configuration or the updated configuration of the
featurestore. For example,
online_serving_config.fixed_node_count
can take minutes
to update. While the update is in progress, the featurestore
is in the UPDATING state, and the value of
fixed_node_count
can be the original value or the
updated value, depending on the progress of the operation.
Until the update completes, the actual number of nodes can
still be the original value of fixed_node_count
. The
featurestore is still usable in this state.
Methods
Featurestore
Featurestore(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.