Lookout for Equipment Model

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Lookout for Equipment Model in AWS is a managed machine learning resource that analyzes sensor data from industrial equipment to detect abnormal behavior. The model is trained on historical time-series data to identify patterns that indicate potential equipment failures. Once deployed, it provides insights that help predict maintenance needs, reduce downtime, and improve operational efficiency.

aws.lookoutequipment_model

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
accumulated_inference_data_end_time	core	timestamp	Indicates the end time of the inference data that has been accumulated.
accumulated_inference_data_start_time	core	timestamp	Indicates the start time of the inference data that has been accumulated.
active_model_version	core	int64	The name of the model version used by the inference schedular when running a scheduled inference execution.
active_model_version_arn	core	string	The Amazon Resource Name (ARN) of the model version used by the inference scheduler when running a scheduled inference execution.
created_at	core	timestamp	Indicates the time and date at which the machine learning model 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 Resouce Name (ARN) of the dataset used to create the machine learning model being described.
dataset_name	core	string	The name of the dataset being used by the machine learning being described.
evaluation_data_end_time	core	timestamp	Indicates the time reference in the dataset that was used to end the subset of evaluation data for the machine learning model.
evaluation_data_start_time	core	timestamp	Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the machine learning model.
failed_reason	core	string	If the training of the machine learning model failed, this indicates the reason for that failure.
import_job_end_time	core	timestamp	The date and time when the import job was completed. This field appears if the active model version was imported.
import_job_start_time	core	timestamp	The date and time when the import job was started. This field appears if the active model version was imported.
labels_input_configuration	core	json	Specifies configuration information about the labels input, including its S3 location.
last_updated_time	core	timestamp	Indicates the last time the machine learning model was updated. The type of update is not specified.
latest_scheduled_retraining_available_data_in_days	core	int64	Indicates the number of days of data used in the most recent scheduled retraining run.
latest_scheduled_retraining_failed_reason	core	string	If the model version was generated by retraining and the training failed, this indicates the reason for that failure.
latest_scheduled_retraining_model_version	core	int64	Indicates the most recent model version that was generated by retraining.
latest_scheduled_retraining_start_time	core	timestamp	Indicates the start time of the most recent scheduled retraining run.
latest_scheduled_retraining_status	core	string	Indicates the status of the most recent scheduled retraining run.
model_arn	core	string	The Amazon Resource Name (ARN) of the machine learning model being described.
model_diagnostics_output_configuration	core	json	Configuration information for the model's pointwise model diagnostics.
model_metrics	core	string	The Model Metrics show an aggregated summary of the model's performance within the evaluation time range. This is the JSON content of the metrics created when evaluating the model.
model_name	core	string	The name of the machine learning model being described.
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_activated_at	core	timestamp	The date the active model version was activated.
next_scheduled_retraining_start_date	core	timestamp	Indicates the date and time that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.
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.
previous_active_model_version	core	int64	The model version that was set as the active model version prior to the current active model version.
previous_active_model_version_arn	core	string	The ARN of the model version that was set as the active model version prior to the current active model version.
previous_model_version_activated_at	core	timestamp	The date and time when the previous active model version was activated.
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_scheduler_status	core	string	Indicates the status of the retraining scheduler.
role_arn	core	string	The Amazon Resource Name (ARN) of a role with permission to access the data source for the machine learning model being described.
schema	core	string	A JSON description of the data that is in each time series dataset, including names, column names, and data types.
server_side_kms_key_id	core	string	Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
source_model_version_arn	core	string	The Amazon Resource Name (ARN) of the source model version. This field appears if the active model version was imported.
status	core	string	Specifies the current status of the model being described. Status describes the status of the most recent action of the model.
tags	core	hstore
training_data_end_time	core	timestamp	Indicates the time reference in the dataset that was used to end the subset of training data for the machine learning model.
training_data_start_time	core	timestamp	Indicates the time reference in the dataset that was used to begin the subset of training data for the machine learning model.
training_execution_end_time	core	timestamp	Indicates the time at which the training of the machine learning model was completed.
training_execution_start_time	core	timestamp	Indicates the time at which the training of the machine learning model began.