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Type: STRING
accumulated_inference_data_end_time
Type: TIMESTAMP
Provider name: AccumulatedInferenceDataEndTime
Description: Indicates the end time of the inference data that has been accumulated.
accumulated_inference_data_start_time
Type: TIMESTAMP
Provider name: AccumulatedInferenceDataStartTime
Description: Indicates the start time of the inference data that has been accumulated.
active_model_version
Type: INT64
Provider name: ActiveModelVersion
Description: The name of the model version used by the inference schedular when running a scheduled inference execution.
active_model_version_arn
Type: STRING
Provider name: ActiveModelVersionArn
Description: The Amazon Resource Name (ARN) of the model version used by the inference scheduler when running a scheduled inference execution.
created_at
Type: TIMESTAMP
Provider name: CreatedAt
Description: Indicates the time and date at which the machine learning model was created.
data_pre_processing_configuration
Type: STRUCT
Provider name: DataPreProcessingConfiguration
Description: 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
target_sampling_rate
STRING
TargetSamplingRate
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 PT1Hdataset_arn
Type: STRING
Provider name: DatasetArn
Description: The Amazon Resouce Name (ARN) of the dataset used to create the machine learning model being described.
dataset_name
Type: STRING
Provider name: DatasetName
Description: The name of the dataset being used by the machine learning being described.
evaluation_data_end_time
Type: TIMESTAMP
Provider name: EvaluationDataEndTime
Description: 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
Type: TIMESTAMP
Provider name: EvaluationDataStartTime
Description: Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the machine learning model.
failed_reason
Type: STRING
Provider name: FailedReason
Description: If the training of the machine learning model failed, this indicates the reason for that failure.
import_job_end_time
Type: TIMESTAMP
Provider name: ImportJobEndTime
Description: The date and time when the import job was completed. This field appears if the active model version was imported.
import_job_start_time
Type: TIMESTAMP
Provider name: ImportJobStartTime
Description: The date and time when the import job was started. This field appears if the active model version was imported.
labels_input_configuration
Type: STRUCT
Provider name: LabelsInputConfiguration
Description: Specifies configuration information about the labels input, including its S3 location.
label_group_name
STRING
LabelGroupName
s3_input_configuration
STRUCT
S3InputConfiguration
bucket
STRING
Bucket
prefix
STRING
Prefix
last_updated_time
Type: TIMESTAMP
Provider name: LastUpdatedTime
Description: Indicates the last time the machine learning model was updated. The type of update is not specified.
latest_scheduled_retraining_available_data_in_days
Type: INT32
Provider name: LatestScheduledRetrainingAvailableDataInDays
Description: Indicates the number of days of data used in the most recent scheduled retraining run.
latest_scheduled_retraining_failed_reason
Type: STRING
Provider name: LatestScheduledRetrainingFailedReason
Description: If the model version was generated by retraining and the training failed, this indicates the reason for that failure.
latest_scheduled_retraining_model_version
Type: INT64
Provider name: LatestScheduledRetrainingModelVersion
Description: Indicates the most recent model version that was generated by retraining.
latest_scheduled_retraining_start_time
Type: TIMESTAMP
Provider name: LatestScheduledRetrainingStartTime
Description: Indicates the start time of the most recent scheduled retraining run.
latest_scheduled_retraining_status
Type: STRING
Provider name: LatestScheduledRetrainingStatus
Description: Indicates the status of the most recent scheduled retraining run.
model_arn
Type: STRING
Provider name: ModelArn
Description: The Amazon Resource Name (ARN) of the machine learning model being described.
model_diagnostics_output_configuration
Type: STRUCT
Provider name: ModelDiagnosticsOutputConfiguration
Description: Configuration information for the model’s pointwise model diagnostics.
kms_key_id
STRING
KmsKeyId
s3_output_configuration
STRUCT
S3OutputConfiguration
bucket
STRING
Bucket
prefix
STRING
Prefix
bucket
). When you call CreateModel
or UpdateModel
, specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz
. When you call DescribeModel
or DescribeModelVersion
, prefix
contains the file path and filename of the model evaluation file.model_metrics
Type: STRING
Provider name: ModelMetrics
Description: 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
Type: STRING
Provider name: ModelName
Description: The name of the machine learning model being described.
model_quality
Type: STRING
Provider name: ModelQuality
Description: 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
Type: TIMESTAMP
Provider name: ModelVersionActivatedAt
Description: The date the active model version was activated.
next_scheduled_retraining_start_date
Type: TIMESTAMP
Provider name: NextScheduledRetrainingStartDate
Description: 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
Type: STRING
Provider name: OffCondition
Description: 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
Type: INT64
Provider name: PreviousActiveModelVersion
Description: The model version that was set as the active model version prior to the current active model version.
previous_active_model_version_arn
Type: STRING
Provider name: PreviousActiveModelVersionArn
Description: 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
Type: TIMESTAMP
Provider name: PreviousModelVersionActivatedAt
Description: The date and time when the previous active model version was activated.
prior_model_metrics
Type: STRING
Provider name: PriorModelMetrics
Description: 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
Type: STRING
Provider name: RetrainingSchedulerStatus
Description: Indicates the status of the retraining scheduler.
role_arn
Type: STRING
Provider name: RoleArn
Description: The Amazon Resource Name (ARN) of a role with permission to access the data source for the machine learning model being described.
schema
Type: STRING
Provider name: Schema
Description: 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
Type: STRING
Provider name: ServerSideKmsKeyId
Description: Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
source_model_version_arn
Type: STRING
Provider name: SourceModelVersionArn
Description: The Amazon Resource Name (ARN) of the source model version. This field appears if the active model version was imported.
status
Type: STRING
Provider name: Status
Description: Specifies the current status of the model being described. Status describes the status of the most recent action of the model.
tags
Type: UNORDERED_LIST_STRING
training_data_end_time
Type: TIMESTAMP
Provider name: TrainingDataEndTime
Description: 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
Type: TIMESTAMP
Provider name: TrainingDataStartTime
Description: 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
Type: TIMESTAMP
Provider name: TrainingExecutionEndTime
Description: Indicates the time at which the training of the machine learning model was completed.
training_execution_start_time
Type: TIMESTAMP
Provider name: TrainingExecutionStartTime
Description: Indicates the time at which the training of the machine learning model began.