Vertex AI Data Labeling Job

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A Vertex AI Data Labeling Job in Google Cloud is a managed service that helps create high-quality labeled datasets for machine learning. It coordinates human labelers or automated labeling tools to annotate data such as images, text, or video according to defined instructions. The labeled output is stored in a dataset for training and evaluation of AI models.

gcp.aiplatform_data_labeling_job

Fields

ID	Type	Data Type	Description
_key	core	string
active_learning_config	core	json	Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
ancestors	core	array<string>
create_time	core	timestamp	Output only. Timestamp when this DataLabelingJob was created.
current_spend	core	json	Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
datadog_display_name	core	string
datasets	core	array<string>	Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
encryption_spec	core	json	Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
error	core	json	Output only. DataLabelingJob errors. It is only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
gcp_display_name	core	string	Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
inputs_schema_uri	core	string	Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
instruction_uri	core	string	Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
labeler_count	core	int64	Required. Number of labelers to work on each DataItem.
labeling_progress	core	int64	Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
labels	core	array<string>	The labels with user-defined metadata to organize your DataLabelingJobs. 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 and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
name	core	string	Output only. Resource name of the DataLabelingJob.
organization_id	core	string
parent	core	string
project_id	core	string
project_number	core	string
region_id	core	string
resource_name	core	string
specialist_pools	core	array<string>	The SpecialistPools' resource names associated with this job.
state	core	string	Output only. The detailed state of the job.
tags	core	hstore_csv
update_time	core	timestamp	Output only. Timestamp when this DataLabelingJob was updated most recently.
zone_id	core	string