Lookout for Equipment Model Version

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Lookout for Equipment Model Version in AWS provides details about a specific version of a machine learning model used for equipment monitoring. It returns information such as the model’s status, training data, performance metrics, and version identifiers. This helps users track model lifecycle, evaluate accuracy, and manage updates for predictive maintenance solutions.

aws.lookoutequipment_model_version

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
auto_promotion_result	core	string	Indicates whether the model version was promoted to be the active version after retraining or if there was an error with or cancellation of the retraining.
auto_promotion_result_reason	core	string	Indicates the reason for the AutoPromotionResult. For example, a model might not be promoted if its performance was worse than the active version, if there was an error during training, or if the retraining scheduler was using MANUAL promote mode. The model will be promoted in MANAGED promote mode if the performance is better than the previous model.
created_at	core	timestamp	Indicates the time and date at which the machine learning model version was created.
data_pre_processing_configuration	core	json	The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute. When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H
dataset_arn	core	string	The Amazon Resource Name (ARN) of the dataset used to train the model version.
dataset_name	core	string	The name of the dataset used to train the model version.
evaluation_data_end_time	core	timestamp	The date on which the data in the evaluation set began being gathered. If you imported the version, this is the date that the evaluation set data in the source version finished being gathered.
evaluation_data_start_time	core	timestamp	The date on which the data in the evaluation set began being gathered. If you imported the version, this is the date that the evaluation set data in the source version began being gathered.
failed_reason	core	string	The failure message if the training of the model version failed.
import_job_end_time	core	timestamp	The date and time when the import job completed. This field appears if the model version was imported.
import_job_start_time	core	timestamp	The date and time when the import job began. This field appears if the model version was imported.
imported_data_size_in_bytes	core	int64	The size in bytes of the imported data. This field appears if the model version was imported.
labels_input_configuration	core	json	Contains the configuration information for the S3 location being used to hold label data.
last_updated_time	core	timestamp	Indicates the last time the machine learning model version was updated.
model_arn	core	string	The Amazon Resource Name (ARN) of the parent machine learning model that this version belong to.
model_diagnostics_output_configuration	core	json	The Amazon S3 location where Amazon Lookout for Equipment saves the pointwise model diagnostics for the model version.
model_diagnostics_results_object	core	json	The Amazon S3 output prefix for where Lookout for Equipment saves the pointwise model diagnostics for the model version.
model_metrics	core	string	Shows an aggregated summary, in JSON format, of the model's performance within the evaluation time range. These metrics are created when evaluating the model.
model_name	core	string	The name of the machine learning model that this version belongs to.
model_quality	core	string	Provides a quality assessment for a model that uses labels. If Lookout for Equipment determines that the model quality is poor based on training metrics, the value is POOR_QUALITY_DETECTED. Otherwise, the value is QUALITY_THRESHOLD_MET. If the model is unlabeled, the model quality can't be assessed and the value of ModelQuality is CANNOT_DETERMINE_QUALITY. In this situation, you can get a model quality assessment by adding labels to the input dataset and retraining the model. For information about using labels with your models, see Understanding labeling. For information about improving the quality of a model, see Best practices with Amazon Lookout for Equipment.
model_version	core	int64	The version of the machine learning model.
model_version_arn	core	string	The Amazon Resource Name (ARN) of the model version.
off_condition	core	string	Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.
prior_model_metrics	core	string	If the model version was retrained, this field shows a summary of the performance of the prior model on the new training range. You can use the information in this JSON-formatted object to compare the new model version and the prior model version.
retraining_available_data_in_days	core	int64	Indicates the number of days of data used in the most recent scheduled retraining run.
role_arn	core	string	The Amazon Resource Name (ARN) of the role that was used to train the model version.
schema	core	string	The schema of the data used to train the model version.
server_side_kms_key_id	core	string	The identifier of the KMS key key used to encrypt model version data by Amazon Lookout for Equipment.
source_model_version_arn	core	string	If model version was imported, then this field is the arn of the source model version.
source_type	core	string	Indicates whether this model version was created by training or by importing.
status	core	string	The current status of the model version.
tags	core	hstore
training_data_end_time	core	timestamp	The date on which the training data finished being gathered. If you imported the version, this is the date that the training data in the source version finished being gathered.
training_data_start_time	core	timestamp	The date on which the training data began being gathered. If you imported the version, this is the date that the training data in the source version began being gathered.
training_execution_end_time	core	timestamp	The time when the training of the version completed.
training_execution_start_time	core	timestamp	The time when the training of the version began.