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

# aws_lookoutequipment_model{% #aws_lookoutequipment_model %}

## `account_id`{% #account_id %}

**Type**: `STRING`

## `accumulated_inference_data_end_time`{% #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`{% #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`{% #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`{% #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`{% #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`{% #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`**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 Resouce Name (ARN) of the dataset used to create the machine learning model being described.

## `dataset_name`{% #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`{% #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`{% #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`{% #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`{% #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`{% #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`{% #labels_input_configuration %}

**Type**: `STRUCT`**Provider name**: `LabelsInputConfiguration`**Description**: Specifies configuration information about the labels input, including its S3 location.

- `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 was updated. The type of update is not specified.

## `latest_scheduled_retraining_available_data_in_days`{% #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`{% #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`{% #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`{% #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`{% #latest_scheduled_retraining_status %}

**Type**: `STRING`**Provider name**: `LatestScheduledRetrainingStatus`**Description**: Indicates the status of the most recent scheduled retraining run.

## `model_arn`{% #model_arn %}

**Type**: `STRING`**Provider name**: `ModelArn`**Description**: The Amazon Resource Name (ARN) of the machine learning model being described.

## `model_diagnostics_output_configuration`{% #model_diagnostics_output_configuration %}

**Type**: `STRUCT`**Provider name**: `ModelDiagnosticsOutputConfiguration`**Description**: Configuration information for the model's pointwise model diagnostics.

- `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_metrics`{% #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`{% #model_name %}

**Type**: `STRING`**Provider name**: `ModelName`**Description**: The name of the machine learning model being described.

## `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_activated_at`{% #model_version_activated_at %}

**Type**: `TIMESTAMP`**Provider name**: `ModelVersionActivatedAt`**Description**: The date the active model version was activated.

## `next_scheduled_retraining_start_date`{% #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`{% #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`{% #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`{% #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`{% #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`{% #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`{% #retraining_scheduler_status %}

**Type**: `STRING`**Provider name**: `RetrainingSchedulerStatus`**Description**: Indicates the status of the retraining scheduler.

## `role_arn`{% #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`{% #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`{% #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`{% #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`{% #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`{% #tags %}

**Type**: `UNORDERED_LIST_STRING`

## `training_data_end_time`{% #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`{% #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`{% #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`{% #training_execution_start_time %}

**Type**: `TIMESTAMP`**Provider name**: `TrainingExecutionStartTime`**Description**: Indicates the time at which the training of the machine learning model began.