---
title: Getting Started with Datadog
description: Datadog, the leading service for cloud-scale monitoring.
breadcrumbs: Docs > Infrastructure > Datadog Resource Catalog
---

# aws_lookoutequipment_model_version{% #aws_lookoutequipment_model_version %}

## `account_id`{% #account_id %}

**Type**: `STRING`

## `auto_promotion_result`{% #auto_promotion_result %}

**Type**: `STRING`**Provider name**: `AutoPromotionResult`**Description**: 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`{% #auto_promotion_result_reason %}

**Type**: `STRING`**Provider name**: `AutoPromotionResultReason`**Description**: 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`{% #created_at %}

**Type**: `TIMESTAMP`**Provider name**: `CreatedAt`**Description**: Indicates the time and date at which the machine learning model version was created.

## `data_pre_processing_configuration`{% #data_pre_processing_configuration %}

**Type**: `STRUCT`**Provider name**: `DataPreProcessingConfiguration`

- `target_sampling_rate`**Type**: `STRING`**Provider name**: `TargetSamplingRate`**Description**: 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`{% #dataset_arn %}

**Type**: `STRING`**Provider name**: `DatasetArn`**Description**: The Amazon Resource Name (ARN) of the dataset used to train the model version.

## `dataset_name`{% #dataset_name %}

**Type**: `STRING`**Provider name**: `DatasetName`**Description**: The name of the dataset used to train the model version.

## `evaluation_data_end_time`{% #evaluation_data_end_time %}

**Type**: `TIMESTAMP`**Provider name**: `EvaluationDataEndTime`**Description**: 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`{% #evaluation_data_start_time %}

**Type**: `TIMESTAMP`**Provider name**: `EvaluationDataStartTime`**Description**: 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`{% #failed_reason %}

**Type**: `STRING`**Provider name**: `FailedReason`**Description**: The failure message if the training of the model version failed.

## `import_job_end_time`{% #import_job_end_time %}

**Type**: `TIMESTAMP`**Provider name**: `ImportJobEndTime`**Description**: The date and time when the import job completed. This field appears if the model version was imported.

## `import_job_start_time`{% #import_job_start_time %}

**Type**: `TIMESTAMP`**Provider name**: `ImportJobStartTime`**Description**: The date and time when the import job began. This field appears if the model version was imported.

## `imported_data_size_in_bytes`{% #imported_data_size_in_bytes %}

**Type**: `INT64`**Provider name**: `ImportedDataSizeInBytes`**Description**: The size in bytes of the imported data. This field appears if the model version was imported.

## `labels_input_configuration`{% #labels_input_configuration %}

**Type**: `STRUCT`**Provider name**: `LabelsInputConfiguration`

- `label_group_name`**Type**: `STRING`**Provider name**: `LabelGroupName`**Description**: The name of the label group to be used for label data.
- `s3_input_configuration`**Type**: `STRUCT`**Provider name**: `S3InputConfiguration`**Description**: Contains location information for the S3 location being used for label data.
  - `bucket`**Type**: `STRING`**Provider name**: `Bucket`**Description**: The name of the S3 bucket holding the label data.
  - `prefix`**Type**: `STRING`**Provider name**: `Prefix`**Description**: The prefix for the S3 bucket used for the label data.

## `last_updated_time`{% #last_updated_time %}

**Type**: `TIMESTAMP`**Provider name**: `LastUpdatedTime`**Description**: Indicates the last time the machine learning model version was updated.

## `model_arn`{% #model_arn %}

**Type**: `STRING`**Provider name**: `ModelArn`**Description**: The Amazon Resource Name (ARN) of the parent machine learning model that this version belong to.

## `model_diagnostics_output_configuration`{% #model_diagnostics_output_configuration %}

**Type**: `STRUCT`**Provider name**: `ModelDiagnosticsOutputConfiguration`**Description**: The Amazon S3 location where Amazon Lookout for Equipment saves the pointwise model diagnostics for the model version.

- `kms_key_id`**Type**: `STRING`**Provider name**: `KmsKeyId`**Description**: The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.
- `s3_output_configuration`**Type**: `STRUCT`**Provider name**: `S3OutputConfiguration`**Description**: The Amazon S3 location for the pointwise model diagnostics.
  - `bucket`**Type**: `STRING`**Provider name**: `Bucket`**Description**: The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.
  - `prefix`**Type**: `STRING`**Provider name**: `Prefix`**Description**: The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. (`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_diagnostics_results_object`{% #model_diagnostics_results_object %}

**Type**: `STRUCT`**Provider name**: `ModelDiagnosticsResultsObject`**Description**: The Amazon S3 output prefix for where Lookout for Equipment saves the pointwise model diagnostics for the model version.

- `bucket`**Type**: `STRING`**Provider name**: `Bucket`**Description**: The name of the specific S3 bucket.
- `key`**Type**: `STRING`**Provider name**: `Key`**Description**: The Amazon Web Services Key Management Service (KMS key) key being used to encrypt the S3 object. Without this key, data in the bucket is not accessible.

## `model_metrics`{% #model_metrics %}

**Type**: `STRING`**Provider name**: `ModelMetrics`**Description**: 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`{% #model_name %}

**Type**: `STRING`**Provider name**: `ModelName`**Description**: The name of the machine learning model that this version belongs to.

## `model_quality`{% #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](https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/understanding-labeling.html). For information about improving the quality of a model, see [Best practices with Amazon Lookout for Equipment](https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/best-practices.html).

## `model_version`{% #model_version %}

**Type**: `INT64`**Provider name**: `ModelVersion`**Description**: The version of the machine learning model.

## `model_version_arn`{% #model_version_arn %}

**Type**: `STRING`**Provider name**: `ModelVersionArn`**Description**: The Amazon Resource Name (ARN) of the model version.

## `off_condition`{% #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.

## `prior_model_metrics`{% #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_available_data_in_days`{% #retraining_available_data_in_days %}

**Type**: `INT32`**Provider name**: `RetrainingAvailableDataInDays`**Description**: Indicates the number of days of data used in the most recent scheduled retraining run.

## `role_arn`{% #role_arn %}

**Type**: `STRING`**Provider name**: `RoleArn`**Description**: The Amazon Resource Name (ARN) of the role that was used to train the model version.

## `schema`{% #schema %}

**Type**: `STRING`**Provider name**: `Schema`**Description**: The schema of the data used to train the model version.

## `server_side_kms_key_id`{% #server_side_kms_key_id %}

**Type**: `STRING`**Provider name**: `ServerSideKmsKeyId`**Description**: The identifier of the KMS key key used to encrypt model version data by Amazon Lookout for Equipment.

## `source_model_version_arn`{% #source_model_version_arn %}

**Type**: `STRING`**Provider name**: `SourceModelVersionArn`**Description**: If model version was imported, then this field is the arn of the source model version.

## `source_type`{% #source_type %}

**Type**: `STRING`**Provider name**: `SourceType`**Description**: Indicates whether this model version was created by training or by importing.

## `status`{% #status %}

**Type**: `STRING`**Provider name**: `Status`**Description**: The current status of the model version.

## `tags`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`

## `training_data_end_time`{% #training_data_end_time %}

**Type**: `TIMESTAMP`**Provider name**: `TrainingDataEndTime`**Description**: 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`{% #training_data_start_time %}

**Type**: `TIMESTAMP`**Provider name**: `TrainingDataStartTime`**Description**: 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`{% #training_execution_end_time %}

**Type**: `TIMESTAMP`**Provider name**: `TrainingExecutionEndTime`**Description**: The time when the training of the version completed.

## `training_execution_start_time`{% #training_execution_start_time %}

**Type**: `TIMESTAMP`**Provider name**: `TrainingExecutionStartTime`**Description**: The time when the training of the version began.