Module Smaws_Client_LookoutEquipment.Types

type nonrec bounded_length_string = string
type nonrec validation_exception = {
  1. message : bounded_length_string;
}

The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized.

type nonrec model_name = string
type nonrec timestamp = Smaws_Lib.CoreTypes.Timestamp.t
type nonrec retraining_frequency = string
type nonrec lookback_window = string
type nonrec model_promote_mode =
  1. | MANUAL
  2. | MANAGED
type nonrec update_retraining_scheduler_request = {
  1. promote_mode : model_promote_mode option;
    (*

    Indicates how the service will use new models. In MANAGED mode, new models will automatically be used for inference if they have better performance than the current model. In MANUAL mode, the new models will not be used until they are manually activated.

    *)
  2. lookback_window : lookback_window option;
    (*

    The number of past days of data that will be used for retraining.

    *)
  3. retraining_frequency : retraining_frequency option;
    (*

    This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:

    • P3M15D – Every 3 months and 15 days
    • P2M – Every 2 months
    • P150D – Every 150 days
    *)
  4. retraining_start_date : timestamp option;
    (*

    The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.

    *)
  5. model_name : model_name;
    (*

    The name of the model whose retraining scheduler you want to update.

    *)
}
type nonrec throttling_exception = {
  1. message : bounded_length_string;
}

The request was denied due to request throttling.

type nonrec resource_not_found_exception = {
  1. message : bounded_length_string;
}

The resource requested could not be found. Verify the resource ID and retry your request.

type nonrec internal_server_exception = {
  1. message : bounded_length_string;
}

Processing of the request has failed because of an unknown error, exception or failure.

type nonrec conflict_exception = {
  1. message : bounded_length_string;
}

The request could not be completed due to a conflict with the current state of the target resource.

type nonrec access_denied_exception = {
  1. message : bounded_length_string;
}

The request could not be completed because you do not have access to the resource.

type nonrec s3_bucket = string
type nonrec s3_prefix = string
type nonrec labels_s3_input_configuration = {
  1. prefix : s3_prefix option;
    (*

    The prefix for the S3 bucket used for the label data.

    *)
  2. bucket : s3_bucket;
    (*

    The name of the S3 bucket holding the label data.

    *)
}

The location information (prefix and bucket name) for the s3 location being used for label data.

type nonrec label_group_name = string
type nonrec labels_input_configuration = {
  1. label_group_name : label_group_name option;
    (*

    The name of the label group to be used for label data.

    *)
  2. s3_input_configuration : labels_s3_input_configuration option;
    (*

    Contains location information for the S3 location being used for label data.

    *)
}

Contains the configuration information for the S3 location being used to hold label data.

type nonrec iam_role_arn = string
type nonrec model_diagnostics_s3_output_configuration = {
  1. prefix : s3_prefix option;
    (*

    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.

    *)
  2. bucket : s3_bucket;
    (*

    The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.

    *)
}

The Amazon S3 location for the pointwise model diagnostics for an Amazon Lookout for Equipment model.

type nonrec name_or_arn = string
type nonrec model_diagnostics_output_configuration = {
  1. kms_key_id : name_or_arn option;
    (*

    The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.

    *)
  2. s3_output_configuration : model_diagnostics_s3_output_configuration;
    (*

    The Amazon S3 location for the pointwise model diagnostics.

    *)
}

Output configuration information for the pointwise model diagnostics for an Amazon Lookout for Equipment model.

type nonrec update_model_request = {
  1. model_diagnostics_output_configuration : model_diagnostics_output_configuration option;
    (*

    The Amazon S3 location where you want Amazon Lookout for Equipment to save the pointwise model diagnostics for the model. You must also specify the RoleArn request parameter.

    *)
  2. role_arn : iam_role_arn option;
    (*

    The ARN of the model to update.

    *)
  3. labels_input_configuration : labels_input_configuration option;
  4. model_name : model_name;
    (*

    The name of the model to update.

    *)
}
type nonrec fault_code = string
type nonrec fault_codes = fault_code list
type nonrec update_label_group_request = {
  1. fault_codes : fault_codes option;
    (*

    Updates the code indicating the type of anomaly associated with the label.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  2. label_group_name : label_group_name;
    (*

    The name of the label group to be updated.

    *)
}
type nonrec inference_scheduler_identifier = string
type nonrec data_delay_offset_in_minutes = Smaws_Lib.CoreTypes.Int64.t
type nonrec data_upload_frequency =
  1. | PT1H
  2. | PT30M
  3. | PT15M
  4. | PT10M
  5. | PT5M
type nonrec inference_s3_input_configuration = {
  1. prefix : s3_prefix option;
    (*

    The prefix for the S3 bucket used for the input data for the inference.

    *)
  2. bucket : s3_bucket;
    (*

    The bucket containing the input dataset for the inference.

    *)
}

Specifies configuration information for the input data for the inference, including input data S3 location.

type nonrec time_zone_offset = string
type nonrec file_name_timestamp_format = string
type nonrec component_timestamp_delimiter = string
type nonrec inference_input_name_configuration = {
  1. component_timestamp_delimiter : component_timestamp_delimiter option;
    (*

    Indicates the delimiter character used between items in the data.

    *)
  2. timestamp_format : file_name_timestamp_format option;
    (*

    The format of the timestamp, whether Epoch time, or standard, with or without hyphens (-).

    *)
}

Specifies configuration information for the input data for the inference, including timestamp format and delimiter.

type nonrec inference_input_configuration = {
  1. inference_input_name_configuration : inference_input_name_configuration option;
    (*

    Specifies configuration information for the input data for the inference, including timestamp format and delimiter.

    *)
  2. input_time_zone_offset : time_zone_offset option;
    (*

    Indicates the difference between your time zone and Coordinated Universal Time (UTC).

    *)
  3. s3_input_configuration : inference_s3_input_configuration option;
    (*

    Specifies configuration information for the input data for the inference, including Amazon S3 location of input data.

    *)
}

Specifies configuration information for the input data for the inference, including Amazon S3 location of input data..

type nonrec inference_s3_output_configuration = {
  1. prefix : s3_prefix option;
    (*

    The prefix for the S3 bucket used for the output results from the inference.

    *)
  2. bucket : s3_bucket;
    (*

    The bucket containing the output results from the inference

    *)
}

Specifies configuration information for the output results from the inference, including output S3 location.

type nonrec inference_output_configuration = {
  1. kms_key_id : name_or_arn option;
    (*

    The ID number for the KMS key key used to encrypt the inference output.

    *)
  2. s3_output_configuration : inference_s3_output_configuration;
    (*

    Specifies configuration information for the output results from for the inference, output S3 location.

    *)
}

Specifies configuration information for the output results from for the inference, including KMS key ID and output S3 location.

type nonrec update_inference_scheduler_request = {
  1. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source for the inference scheduler.

    *)
  2. data_output_configuration : inference_output_configuration option;
    (*

    Specifies information for the output results from the inference scheduler, including the output S3 location.

    *)
  3. data_input_configuration : inference_input_configuration option;
    (*

    Specifies information for the input data for the inference scheduler, including delimiter, format, and dataset location.

    *)
  4. data_upload_frequency : data_upload_frequency option;
    (*

    How often data is uploaded to the source S3 bucket for the input data. The value chosen is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes.

    *)
  5. data_delay_offset_in_minutes : data_delay_offset_in_minutes option;
    (*

    A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if you select an offset delay time of five minutes, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.

    *)
  6. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler to be updated.

    *)
}
type nonrec model_arn = string
type nonrec model_version = Smaws_Lib.CoreTypes.Int64.t
type nonrec model_version_arn = string
type nonrec update_active_model_version_response = {
  1. previous_active_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model version that was the previous active model version.

    *)
  2. current_active_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model version that is the current active model version.

    *)
  3. previous_active_version : model_version option;
    (*

    The previous version that was active of the machine learning model for which the active model version was set.

    *)
  4. current_active_version : model_version option;
    (*

    The version that is currently active of the machine learning model for which the active model version was set.

    *)
  5. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model for which the active model version was set.

    *)
  6. model_name : model_name option;
    (*

    The name of the machine learning model for which the active model version was set.

    *)
}
type nonrec update_active_model_version_request = {
  1. model_version : model_version;
    (*

    The version of the machine learning model for which the active model version is being set.

    *)
  2. model_name : model_name;
    (*

    The name of the machine learning model for which the active model version is being set.

    *)
}
type nonrec untag_resource_response = unit
type nonrec amazon_resource_arn = string
type nonrec tag_key = string
type nonrec tag_key_list = tag_key list
type nonrec untag_resource_request = {
  1. tag_keys : tag_key_list;
    (*

    Specifies the key of the tag to be removed from a specified resource.

    *)
  2. resource_arn : amazon_resource_arn;
    (*

    The Amazon Resource Name (ARN) of the resource to which the tag is currently associated.

    *)
}
type nonrec integer = int
type nonrec unsupported_timestamps = {
  1. total_number_of_unsupported_timestamps : integer;
    (*

    Indicates the total number of unsupported timestamps across the ingested data.

    *)
}

Entity that comprises information abount unsupported timestamps in the dataset.

type nonrec target_sampling_rate =
  1. | PT1H
  2. | PT30M
  3. | PT15M
  4. | PT10M
  5. | PT5M
  6. | PT1M
  7. | PT30S
  8. | PT15S
  9. | PT10S
  10. | PT5S
  11. | PT1S
type nonrec tag_value = string
type nonrec tag_resource_response = unit
type nonrec tag = {
  1. value : tag_value;
    (*

    The value for the specified tag.

    *)
  2. key : tag_key;
    (*

    The key for the specified tag.

    *)
}

A tag is a key-value pair that can be added to a resource as metadata.

type nonrec tag_list = tag list
type nonrec tag_resource_request = {
  1. tags : tag_list;
    (*

    The tag or tags to be associated with a specific resource. Both the tag key and value are specified.

    *)
  2. resource_arn : amazon_resource_arn;
    (*

    The Amazon Resource Name (ARN) of the specific resource to which the tag should be associated.

    *)
}
type nonrec service_quota_exceeded_exception = {
  1. message : bounded_length_string;
}

Resource limitations have been exceeded.

type nonrec synthesized_json_model_metrics = string
type nonrec synthesized_json_inline_data_schema = string
type nonrec retraining_scheduler_status =
  1. | STOPPED
  2. | STOPPING
  3. | RUNNING
  4. | PENDING
type nonrec stop_retraining_scheduler_response = {
  1. status : retraining_scheduler_status option;
    (*

    The status of the retraining scheduler.

    *)
  2. model_arn : model_arn option;
    (*

    The ARN of the model whose retraining scheduler is being stopped.

    *)
  3. model_name : model_name option;
    (*

    The name of the model whose retraining scheduler is being stopped.

    *)
}
type nonrec stop_retraining_scheduler_request = {
  1. model_name : model_name;
    (*

    The name of the model whose retraining scheduler you want to stop.

    *)
}
type nonrec inference_scheduler_name = string
type nonrec inference_scheduler_arn = string
type nonrec inference_scheduler_status =
  1. | STOPPED
  2. | STOPPING
  3. | RUNNING
  4. | PENDING
type nonrec stop_inference_scheduler_response = {
  1. status : inference_scheduler_status option;
    (*

    Indicates the status of the inference scheduler.

    *)
  2. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference schedule being stopped.

    *)
  3. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of the inference scheduler being stopped.

    *)
  4. model_name : model_name option;
    (*

    The name of the machine learning model used by the inference scheduler being stopped.

    *)
  5. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model used by the inference scheduler being stopped.

    *)
}
type nonrec stop_inference_scheduler_request = {
  1. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler to be stopped.

    *)
}
type nonrec statistical_issue_status =
  1. | NO_ISSUE_DETECTED
  2. | POTENTIAL_ISSUE_DETECTED
type nonrec start_retraining_scheduler_response = {
  1. status : retraining_scheduler_status option;
    (*

    The status of the retraining scheduler.

    *)
  2. model_arn : model_arn option;
    (*

    The ARN of the model whose retraining scheduler is being started.

    *)
  3. model_name : model_name option;
    (*

    The name of the model whose retraining scheduler is being started.

    *)
}
type nonrec start_retraining_scheduler_request = {
  1. model_name : model_name;
    (*

    The name of the model whose retraining scheduler you want to start.

    *)
}
type nonrec start_inference_scheduler_response = {
  1. status : inference_scheduler_status option;
    (*

    Indicates the status of the inference scheduler.

    *)
  2. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler being started.

    *)
  3. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of the inference scheduler being started.

    *)
  4. model_name : model_name option;
    (*

    The name of the machine learning model being used by the inference scheduler.

    *)
  5. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model being used by the inference scheduler.

    *)
}
type nonrec start_inference_scheduler_request = {
  1. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler to be started.

    *)
}
type nonrec ingestion_job_id = string
type nonrec ingestion_job_status =
  1. | IMPORT_IN_PROGRESS
  2. | FAILED
  3. | SUCCESS
  4. | IN_PROGRESS
type nonrec start_data_ingestion_job_response = {
  1. status : ingestion_job_status option;
    (*

    Indicates the status of the StartDataIngestionJob operation.

    *)
  2. job_id : ingestion_job_id option;
    (*

    Indicates the job ID of the data ingestion job.

    *)
}
type nonrec dataset_identifier = string
type nonrec key_pattern = string
type nonrec ingestion_s3_input_configuration = {
  1. key_pattern : key_pattern option;
    (*

    The pattern for matching the Amazon S3 files that will be used for ingestion. If the schema was created previously without any KeyPattern, then the default KeyPattern {prefix}/{component_name}/* is used to download files from Amazon S3 according to the schema. This field is required when ingestion is being done for the first time.

    Valid Values: {prefix}/{component_name}_* | {prefix}/{component_name}/* | {prefix}/{component_name}[DELIMITER]* (Allowed delimiters : space, dot, underscore, hyphen)

    *)
  2. prefix : s3_prefix option;
    (*

    The prefix for the S3 location being used for the input data for the data ingestion.

    *)
  3. bucket : s3_bucket;
    (*

    The name of the S3 bucket used for the input data for the data ingestion.

    *)
}

Specifies S3 configuration information for the input data for the data ingestion job.

type nonrec ingestion_input_configuration = {
  1. s3_input_configuration : ingestion_s3_input_configuration;
    (*

    The location information for the S3 bucket used for input data for the data ingestion.

    *)
}

Specifies configuration information for the input data for the data ingestion job, including input data S3 location.

type nonrec idempotence_token = string
type nonrec start_data_ingestion_job_request = {
  1. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  2. role_arn : iam_role_arn;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source for the data ingestion job.

    *)
  3. ingestion_input_configuration : ingestion_input_configuration;
    (*

    Specifies information for the input data for the data ingestion job, including dataset S3 location.

    *)
  4. dataset_name : dataset_identifier;
    (*

    The name of the dataset being used by the data ingestion job.

    *)
}
type nonrec sensors_with_short_date_range = {
  1. affected_sensor_count : integer;
    (*

    Indicates the number of sensors that have less than 14 days of data.

    *)
}

Entity that comprises information on sensors that have shorter date range.

type nonrec component_name = string
type nonrec sensor_name = string
type nonrec boolean_ = bool
type nonrec float_ = float
type nonrec count_percent = {
  1. percentage : float_;
    (*

    Indicates the percentage of occurances of the given statistic.

    *)
  2. count : integer;
    (*

    Indicates the count of occurences of the given statistic.

    *)
}

Entity that comprises information of count and percentage.

type nonrec categorical_values = {
  1. number_of_category : integer option;
    (*

    Indicates the number of categories in the data.

    *)
  2. status : statistical_issue_status;
    (*

    Indicates whether there is a potential data issue related to categorical values.

    *)
}

Entity that comprises information on categorical values in data.

type nonrec multiple_operating_modes = {
  1. status : statistical_issue_status;
    (*

    Indicates whether there is a potential data issue related to having multiple operating modes.

    *)
}

Entity that comprises information on operating modes in data.

type nonrec large_timestamp_gaps = {
  1. max_timestamp_gap_in_days : integer option;
    (*

    Indicates the size of the largest timestamp gap, in days.

    *)
  2. number_of_large_timestamp_gaps : integer option;
    (*

    Indicates the number of large timestamp gaps, if there are any.

    *)
  3. status : statistical_issue_status;
    (*

    Indicates whether there is a potential data issue related to large gaps in timestamps.

    *)
}

Entity that comprises information on large gaps between consecutive timestamps in data.

type nonrec monotonicity =
  1. | STATIC
  2. | INCREASING
  3. | DECREASING
type nonrec monotonic_values = {
  1. monotonicity : monotonicity option;
    (*

    Indicates the monotonicity of values. Can be INCREASING, DECREASING, or STATIC.

    *)
  2. status : statistical_issue_status;
    (*

    Indicates whether there is a potential data issue related to having monotonic values.

    *)
}

Entity that comprises information on monotonic values in the data.

type nonrec sensor_statistics_summary = {
  1. data_end_time : timestamp option;
    (*

    Indicates the time reference to indicate the end of valid data associated with the sensor that the statistics belong to.

    *)
  2. data_start_time : timestamp option;
    (*

    Indicates the time reference to indicate the beginning of valid data associated with the sensor that the statistics belong to.

    *)
  3. monotonic_values : monotonic_values option;
    (*

    Parameter that describes potential risk about whether data associated with the sensor is mostly monotonic.

    *)
  4. large_timestamp_gaps : large_timestamp_gaps option;
    (*

    Parameter that describes potential risk about whether data associated with the sensor contains one or more large gaps between consecutive timestamps.

    *)
  5. multiple_operating_modes : multiple_operating_modes option;
    (*

    Parameter that describes potential risk about whether data associated with the sensor has more than one operating mode.

    *)
  6. categorical_values : categorical_values option;
    (*

    Parameter that describes potential risk about whether data associated with the sensor is categorical.

    *)
  7. duplicate_timestamps : count_percent option;
    (*

    Parameter that describes the total number of duplicate timestamp records associated with the sensor that the statistics belong to.

    *)
  8. invalid_date_entries : count_percent option;
    (*

    Parameter that describes the total number of invalid date entries associated with the sensor that the statistics belong to.

    *)
  9. invalid_values : count_percent option;
    (*

    Parameter that describes the total number of, and percentage of, values that are invalid for the sensor that the statistics belong to.

    *)
  10. missing_values : count_percent option;
    (*

    Parameter that describes the total number of, and percentage of, values that are missing for the sensor that the statistics belong to.

    *)
  11. data_exists : boolean_ option;
    (*

    Parameter that indicates whether data exists for the sensor that the statistics belong to.

    *)
  12. sensor_name : sensor_name option;
    (*

    Name of the sensor that the statistics belong to.

    *)
  13. component_name : component_name option;
    (*

    Name of the component to which the particular sensor belongs for which the statistics belong to.

    *)
}

Summary of ingestion statistics like whether data exists, number of missing values, number of invalid values and so on related to the particular sensor.

type nonrec sensor_statistics_summaries = sensor_statistics_summary list
type nonrec s3_key = string
type nonrec s3_object = {
  1. key : s3_key;
    (*

    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.

    *)
  2. bucket : s3_bucket;
    (*

    The name of the specific S3 bucket.

    *)
}

Contains information about an S3 bucket.

type nonrec retraining_scheduler_summary = {
  1. lookback_window : lookback_window option;
    (*

    The number of past days of data used for retraining.

    *)
  2. retraining_frequency : retraining_frequency option;
    (*

    The frequency at which the model retraining is set. This follows the ISO 8601 guidelines.

    *)
  3. retraining_start_date : timestamp option;
    (*

    The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.

    *)
  4. status : retraining_scheduler_status option;
    (*

    The status of the retraining scheduler.

    *)
  5. model_arn : model_arn option;
    (*

    The ARN of the model that the retraining scheduler is attached to.

    *)
  6. model_name : model_name option;
    (*

    The name of the model that the retraining scheduler is attached to.

    *)
}

Provides information about the specified retraining scheduler, including model name, status, start date, frequency, and lookback window.

type nonrec retraining_scheduler_summaries = retraining_scheduler_summary list
type nonrec resource_arn = string
type nonrec policy_revision_id = string
type nonrec put_resource_policy_response = {
  1. policy_revision_id : policy_revision_id option;
    (*

    A unique identifier for a revision of the resource policy.

    *)
  2. resource_arn : resource_arn option;
    (*

    The Amazon Resource Name (ARN) of the resource for which the policy was created.

    *)
}
type nonrec policy = string
type nonrec put_resource_policy_request = {
  1. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  2. policy_revision_id : policy_revision_id option;
    (*

    A unique identifier for a revision of the resource policy.

    *)
  3. resource_policy : policy;
    (*

    The JSON-formatted resource policy to create.

    *)
  4. resource_arn : resource_arn;
    (*

    The Amazon Resource Name (ARN) of the resource for which the policy is being created.

    *)
}
type nonrec off_condition = string
type nonrec next_token = string
type nonrec model_version_status =
  1. | CANCELED
  2. | IMPORT_IN_PROGRESS
  3. | FAILED
  4. | SUCCESS
  5. | IN_PROGRESS
type nonrec model_version_source_type =
  1. | IMPORT
  2. | RETRAINING
  3. | TRAINING
type nonrec model_quality =
  1. | POOR_QUALITY_DETECTED
  2. | CANNOT_DETERMINE_QUALITY
  3. | QUALITY_THRESHOLD_MET
type nonrec model_version_summary = {
  1. model_quality : model_quality option;
    (*

    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 improving the quality of a model, see Best practices with Amazon Lookout for Equipment.

    *)
  2. source_type : model_version_source_type option;
    (*

    Indicates how this model version was generated.

    *)
  3. status : model_version_status option;
    (*

    The current status of the model version.

    *)
  4. created_at : timestamp option;
    (*

    The time when this model version was created.

    *)
  5. model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the model version.

    *)
  6. model_version : model_version option;
    (*

    The version of the model.

    *)
  7. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the model that this model version is a version of.

    *)
  8. model_name : model_name option;
    (*

    The name of the model that this model version is a version of.

    *)
}

Contains information about the specific model version.

type nonrec model_version_summaries = model_version_summary list
type nonrec dataset_name = string
type nonrec dataset_arn = string
type nonrec model_status =
  1. | IMPORT_IN_PROGRESS
  2. | FAILED
  3. | SUCCESS
  4. | IN_PROGRESS
type nonrec model_summary = {
  1. model_quality : model_quality option;
    (*

    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.

    *)
  2. model_diagnostics_output_configuration : model_diagnostics_output_configuration option;
  3. retraining_scheduler_status : retraining_scheduler_status option;
    (*

    Indicates the status of the retraining scheduler.

    *)
  4. next_scheduled_retraining_start_date : timestamp option;
    (*

    Indicates the date that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.

    *)
  5. latest_scheduled_retraining_start_time : timestamp option;
    (*

    Indicates the start time of the most recent scheduled retraining run.

    *)
  6. latest_scheduled_retraining_model_version : model_version option;
    (*

    Indicates the most recent model version that was generated by retraining.

    *)
  7. latest_scheduled_retraining_status : model_version_status option;
    (*

    Indicates the status of the most recent scheduled retraining run.

    *)
  8. active_model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the model version that is set as active. The active model version is the model version that the inference scheduler uses to run an inference execution.

    *)
  9. active_model_version : model_version option;
    (*

    The model version that the inference scheduler uses to run an inference execution.

    *)
  10. created_at : timestamp option;
    (*

    The time at which the specific model was created.

    *)
  11. status : model_status option;
    (*

    Indicates the status of the machine learning model.

    *)
  12. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset used to create the model.

    *)
  13. dataset_name : dataset_name option;
    (*

    The name of the dataset being used for the machine learning model.

    *)
  14. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model.

    *)
  15. model_name : model_name option;
    (*

    The name of the machine learning model.

    *)
}

Provides information about the specified machine learning model, including dataset and model names and ARNs, as well as status.

type nonrec model_summaries = model_summary list
type nonrec model_metrics = string
type nonrec missing_sensor_data = {
  1. total_number_of_missing_values : integer;
    (*

    Indicates the total number of missing values across all the sensors.

    *)
  2. affected_sensor_count : integer;
    (*

    Indicates the number of sensors that have atleast some data missing.

    *)
}

Entity that comprises aggregated information on sensors having missing data.

type nonrec missing_complete_sensor_data = {
  1. affected_sensor_count : integer;
    (*

    Indicates the number of sensors that have data missing completely.

    *)
}

Entity that comprises information on sensors that have sensor data completely missing.

type nonrec max_results = int
type nonrec list_tags_for_resource_response = {
  1. tags : tag_list option;
    (*

    Any tags associated with the resource.

    *)
}
type nonrec list_tags_for_resource_request = {
  1. resource_arn : amazon_resource_arn;
    (*

    The Amazon Resource Name (ARN) of the resource (such as the dataset or model) that is the focus of the ListTagsForResource operation.

    *)
}
type nonrec list_sensor_statistics_response = {
  1. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of sensor statistics.

    *)
  2. sensor_statistics_summaries : sensor_statistics_summaries option;
    (*

    Provides ingestion-based statistics regarding the specified sensor with respect to various validation types, such as whether data exists, the number and percentage of missing values, and the number and percentage of duplicate timestamps.

    *)
}
type nonrec list_sensor_statistics_request = {
  1. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of sensor statistics.

    *)
  2. max_results : max_results option;
    (*

    Specifies the maximum number of sensors for which to retrieve statistics.

    *)
  3. ingestion_job_id : ingestion_job_id option;
    (*

    The ingestion job id associated with the list of Sensor Statistics. To get sensor statistics for a particular ingestion job id, both dataset name and ingestion job id must be submitted as inputs.

    *)
  4. dataset_name : dataset_name;
    (*

    The name of the dataset associated with the list of Sensor Statistics.

    *)
}
type nonrec list_retraining_schedulers_response = {
  1. next_token : next_token option;
    (*

    If the number of results exceeds the maximum, this pagination token is returned. Use this token in the request to show the next page of retraining schedulers.

    *)
  2. retraining_scheduler_summaries : retraining_scheduler_summaries option;
    (*

    Provides information on the specified retraining scheduler, including the model name, model ARN, status, and start date.

    *)
}
type nonrec list_retraining_schedulers_request = {
  1. max_results : max_results option;
    (*

    Specifies the maximum number of retraining schedulers to list.

    *)
  2. next_token : next_token option;
    (*

    If the number of results exceeds the maximum, a pagination token is returned. Use the token in the request to show the next page of retraining schedulers.

    *)
  3. status : retraining_scheduler_status option;
    (*

    Specify this field to only list retraining schedulers whose status matches the value you specify.

    *)
  4. model_name_begins_with : model_name option;
    (*

    Specify this field to only list retraining schedulers whose machine learning models begin with the value you specify.

    *)
}
type nonrec list_of_discarded_files = s3_object list
type nonrec list_models_response = {
  1. model_summaries : model_summaries option;
    (*

    Provides information on the specified model, including created time, model and dataset ARNs, and status.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of machine learning models.

    *)
}
type nonrec list_models_request = {
  1. dataset_name_begins_with : dataset_name option;
    (*

    The beginning of the name of the dataset of the machine learning models to be listed.

    *)
  2. model_name_begins_with : model_name option;
    (*

    The beginning of the name of the machine learning models being listed.

    *)
  3. status : model_status option;
    (*

    The status of the machine learning model.

    *)
  4. max_results : max_results option;
    (*

    Specifies the maximum number of machine learning models to list.

    *)
  5. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of machine learning models.

    *)
}
type nonrec list_model_versions_response = {
  1. model_version_summaries : model_version_summaries option;
    (*

    Provides information on the specified model version, including the created time, model and dataset ARNs, and status.

    If you don't supply the ModelName request parameter, or if you supply the name of a model that doesn't exist, ListModelVersions returns an empty array in ModelVersionSummaries.

    *)
  2. next_token : next_token option;
    (*

    If the total number of results exceeds the limit that the response can display, the response returns an opaque pagination token indicating where to continue the listing of machine learning model versions. Use this token in the NextToken field in the request to list the next page of results.

    *)
}
type nonrec list_model_versions_request = {
  1. min_model_version : model_version option;
    (*

    Specifies the lowest version of the model to return in the list.

    *)
  2. max_model_version : model_version option;
    (*

    Specifies the highest version of the model to return in the list.

    *)
  3. created_at_start_time : timestamp option;
    (*

    Filter results to return all the model versions created after this time.

    *)
  4. created_at_end_time : timestamp option;
    (*

    Filter results to return all the model versions created before this time.

    *)
  5. source_type : model_version_source_type option;
    (*

    Filter the results based on the way the model version was generated.

    *)
  6. status : model_version_status option;
    (*

    Filter the results based on the current status of the model version.

    *)
  7. max_results : max_results option;
    (*

    Specifies the maximum number of machine learning model versions to list.

    *)
  8. next_token : next_token option;
    (*

    If the total number of results exceeds the limit that the response can display, the response returns an opaque pagination token indicating where to continue the listing of machine learning model versions. Use this token in the NextToken field in the request to list the next page of results.

    *)
  9. model_name : model_name;
    (*

    Then name of the machine learning model for which the model versions are to be listed.

    *)
}
type nonrec label_id = string
type nonrec label_group_arn = string
type nonrec label_rating =
  1. | NEUTRAL
  2. | NO_ANOMALY
  3. | ANOMALY
type nonrec equipment = string
type nonrec label_summary = {
  1. created_at : timestamp option;
    (*

    The time at which the label was created.

    *)
  2. equipment : equipment option;
    (*

    Indicates that a label pertains to a particular piece of equipment.

    *)
  3. fault_code : fault_code option;
    (*

    Indicates the type of anomaly associated with the label.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  4. rating : label_rating option;
    (*

    Indicates whether a labeled event represents an anomaly.

    *)
  5. end_time : timestamp option;
    (*

    The timestamp indicating the end of the label.

    *)
  6. start_time : timestamp option;
    (*

    The timestamp indicating the start of the label.

    *)
  7. label_group_arn : label_group_arn option;
    (*

    The Amazon Resource Name (ARN) of the label group.

    *)
  8. label_id : label_id option;
    (*

    The ID of the label.

    *)
  9. label_group_name : label_group_name option;
    (*

    The name of the label group.

    *)
}

Information about the label.

type nonrec label_summaries = label_summary list
type nonrec list_labels_response = {
  1. label_summaries : label_summaries option;
    (*

    A summary of the items in the label group.

    If you don't supply the LabelGroupName request parameter, or if you supply the name of a label group that doesn't exist, ListLabels returns an empty array in LabelSummaries.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of datasets.

    *)
}
type nonrec list_labels_request = {
  1. max_results : max_results option;
    (*

    Specifies the maximum number of labels to list.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of label groups.

    *)
  3. equipment : equipment option;
    (*

    Lists the labels that pertain to a particular piece of equipment.

    *)
  4. fault_code : fault_code option;
    (*

    Returns labels with a particular fault code.

    *)
  5. interval_end_time : timestamp option;
    (*

    Returns all labels with a start time earlier than the end time given.

    *)
  6. interval_start_time : timestamp option;
    (*

    Returns all the labels with a end time equal to or later than the start time given.

    *)
  7. label_group_name : label_group_name;
    (*

    Returns the name of the label group.

    *)
}
type nonrec label_group_summary = {
  1. updated_at : timestamp option;
    (*

    The time at which the label group was updated.

    *)
  2. created_at : timestamp option;
    (*

    The time at which the label group was created.

    *)
  3. label_group_arn : label_group_arn option;
    (*

    The Amazon Resource Name (ARN) of the label group.

    *)
  4. label_group_name : label_group_name option;
    (*

    The name of the label group.

    *)
}

Contains information about the label group.

type nonrec label_group_summaries = label_group_summary list
type nonrec list_label_groups_response = {
  1. label_group_summaries : label_group_summaries option;
    (*

    A summary of the label groups.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of label groups.

    *)
}
type nonrec list_label_groups_request = {
  1. max_results : max_results option;
    (*

    Specifies the maximum number of label groups to list.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of label groups.

    *)
  3. label_group_name_begins_with : label_group_name option;
    (*

    The beginning of the name of the label groups to be listed.

    *)
}
type nonrec latest_inference_result =
  1. | NORMAL
  2. | ANOMALOUS
type nonrec inference_scheduler_summary = {
  1. latest_inference_result : latest_inference_result option;
    (*

    Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found).

    *)
  2. data_upload_frequency : data_upload_frequency option;
    (*

    How often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes.

    *)
  3. data_delay_offset_in_minutes : data_delay_offset_in_minutes option;
    (*

    A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if an offset delay time of five minutes was selected, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.

    *)
  4. status : inference_scheduler_status option;
    (*

    Indicates the status of the inference scheduler.

    *)
  5. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler.

    *)
  6. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of the inference scheduler.

    *)
  7. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model used by the inference scheduler.

    *)
  8. model_name : model_name option;
    (*

    The name of the machine learning model used for the inference scheduler.

    *)
}

Contains information about the specific inference scheduler, including data delay offset, model name and ARN, status, and so on.

type nonrec inference_scheduler_summaries = inference_scheduler_summary list
type nonrec list_inference_schedulers_response = {
  1. inference_scheduler_summaries : inference_scheduler_summaries option;
    (*

    Provides information about the specified inference scheduler, including data upload frequency, model name and ARN, and status.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of inference schedulers.

    *)
}
type nonrec list_inference_schedulers_request = {
  1. status : inference_scheduler_status option;
    (*

    Specifies the current status of the inference schedulers.

    *)
  2. model_name : model_name option;
    (*

    The name of the machine learning model used by the inference scheduler to be listed.

    *)
  3. inference_scheduler_name_begins_with : inference_scheduler_identifier option;
    (*

    The beginning of the name of the inference schedulers to be listed.

    *)
  4. max_results : max_results option;
    (*

    Specifies the maximum number of inference schedulers to list.

    *)
  5. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of inference schedulers.

    *)
}
type nonrec inference_execution_status =
  1. | FAILED
  2. | SUCCESS
  3. | IN_PROGRESS
type nonrec inference_execution_summary = {
  1. model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Number (ARN) of the model version used for the inference execution.

    *)
  2. model_version : model_version option;
    (*

    The model version used for the inference execution.

    *)
  3. failed_reason : bounded_length_string option;
    (*

    Specifies the reason for failure when an inference execution has failed.

    *)
  4. status : inference_execution_status option;
    (*

    Indicates the status of the inference execution.

    *)
  5. customer_result_object : s3_object option;
    (*

    The S3 object that the inference execution results were uploaded to.

    *)
  6. data_output_configuration : inference_output_configuration option;
    (*

    Specifies configuration information for the output results from for the inference execution, including the output Amazon S3 location.

    *)
  7. data_input_configuration : inference_input_configuration option;
    (*

    Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location.

    *)
  8. data_end_time : timestamp option;
    (*

    Indicates the time reference in the dataset at which the inference execution stopped.

    *)
  9. data_start_time : timestamp option;
    (*

    Indicates the time reference in the dataset at which the inference execution began.

    *)
  10. scheduled_start_time : timestamp option;
    (*

    Indicates the start time at which the inference scheduler began the specific inference execution.

    *)
  11. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler being used for the inference execution.

    *)
  12. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of the inference scheduler being used for the inference execution.

    *)
  13. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model used for the inference execution.

    *)
  14. model_name : model_name option;
    (*

    The name of the machine learning model being used for the inference execution.

    *)
}

Contains information about the specific inference execution, including input and output data configuration, inference scheduling information, status, and so on.

type nonrec inference_execution_summaries = inference_execution_summary list
type nonrec list_inference_executions_response = {
  1. inference_execution_summaries : inference_execution_summaries option;
    (*

    Provides an array of information about the individual inference executions returned from the ListInferenceExecutions operation, including model used, inference scheduler, data configuration, and so on.

    If you don't supply the InferenceSchedulerName request parameter, or if you supply the name of an inference scheduler that doesn't exist, ListInferenceExecutions returns an empty array in InferenceExecutionSummaries.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of inference executions.

    *)
}
type nonrec list_inference_executions_request = {
  1. status : inference_execution_status option;
    (*

    The status of the inference execution.

    *)
  2. data_end_time_before : timestamp option;
    (*

    The time reference in the inferenced dataset before which Amazon Lookout for Equipment stopped the inference execution.

    *)
  3. data_start_time_after : timestamp option;
    (*

    The time reference in the inferenced dataset after which Amazon Lookout for Equipment started the inference execution.

    *)
  4. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler for the inference execution listed.

    *)
  5. max_results : max_results option;
    (*

    Specifies the maximum number of inference executions to list.

    *)
  6. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of inference executions.

    *)
}
type nonrec event_duration_in_seconds = Smaws_Lib.CoreTypes.Int64.t
type nonrec inference_event_summary = {
  1. event_duration_in_seconds : event_duration_in_seconds option;
    (*

    Indicates the size of an inference event in seconds.

    *)
  2. diagnostics : model_metrics option;
    (*

    An array which specifies the names and values of all sensors contributing to an inference event.

    *)
  3. event_end_time : timestamp option;
    (*

    Indicates the ending time of an inference event.

    *)
  4. event_start_time : timestamp option;
    (*

    Indicates the starting time of an inference event.

    *)
  5. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of the inference scheduler being used for the inference events.

    *)
  6. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler being used for the inference event.

    *)
}

Contains information about the specific inference event, including start and end time, diagnostics information, event duration and so on.

type nonrec inference_event_summaries = inference_event_summary list
type nonrec list_inference_events_response = {
  1. inference_event_summaries : inference_event_summaries option;
    (*

    Provides an array of information about the individual inference events returned from the ListInferenceEvents operation, including scheduler used, event start time, event end time, diagnostics, and so on.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of inference executions.

    *)
}
type nonrec list_inference_events_request = {
  1. interval_end_time : timestamp;
    (*

    Returns all the inference events with an end start time equal to or greater than less than the end time given.

    *)
  2. interval_start_time : timestamp;
    (*

    Lookout for Equipment will return all the inference events with an end time equal to or greater than the start time given.

    *)
  3. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler for the inference events listed.

    *)
  4. max_results : max_results option;
    (*

    Specifies the maximum number of inference events to list.

    *)
  5. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of inference events.

    *)
}
type nonrec dataset_status =
  1. | IMPORT_IN_PROGRESS
  2. | ACTIVE
  3. | INGESTION_IN_PROGRESS
  4. | CREATED
type nonrec dataset_summary = {
  1. created_at : timestamp option;
    (*

    The time at which the dataset was created in Amazon Lookout for Equipment.

    *)
  2. status : dataset_status option;
    (*

    Indicates the status of the dataset.

    *)
  3. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the specified dataset.

    *)
  4. dataset_name : dataset_name option;
    (*

    The name of the dataset.

    *)
}

Contains information about the specific data set, including name, ARN, and status.

type nonrec dataset_summaries = dataset_summary list
type nonrec list_datasets_response = {
  1. dataset_summaries : dataset_summaries option;
    (*

    Provides information about the specified dataset, including creation time, dataset ARN, and status.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of datasets.

    *)
}
type nonrec list_datasets_request = {
  1. dataset_name_begins_with : dataset_name option;
    (*

    The beginning of the name of the datasets to be listed.

    *)
  2. max_results : max_results option;
    (*

    Specifies the maximum number of datasets to list.

    *)
  3. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of datasets.

    *)
}
type nonrec data_ingestion_job_summary = {
  1. status : ingestion_job_status option;
    (*

    Indicates the status of the data ingestion job.

    *)
  2. ingestion_input_configuration : ingestion_input_configuration option;
    (*

    Specifies information for the input data for the data inference job, including data Amazon S3 location parameters.

    *)
  3. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset used in the data ingestion job.

    *)
  4. dataset_name : dataset_name option;
    (*

    The name of the dataset used for the data ingestion job.

    *)
  5. job_id : ingestion_job_id option;
    (*

    Indicates the job ID of the data ingestion job.

    *)
}

Provides information about a specified data ingestion job, including dataset information, data ingestion configuration, and status.

type nonrec data_ingestion_job_summaries = data_ingestion_job_summary list
type nonrec list_data_ingestion_jobs_response = {
  1. data_ingestion_job_summaries : data_ingestion_job_summaries option;
    (*

    Specifies information about the specific data ingestion job, including dataset name and status.

    *)
  2. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of data ingestion jobs.

    *)
}
type nonrec list_data_ingestion_jobs_request = {
  1. status : ingestion_job_status option;
    (*

    Indicates the status of the data ingestion job.

    *)
  2. max_results : max_results option;
    (*

    Specifies the maximum number of data ingestion jobs to list.

    *)
  3. next_token : next_token option;
    (*

    An opaque pagination token indicating where to continue the listing of data ingestion jobs.

    *)
  4. dataset_name : dataset_name option;
    (*

    The name of the dataset being used for the data ingestion job.

    *)
}
type nonrec kms_key_arn = string
type nonrec invalid_sensor_data = {
  1. total_number_of_invalid_values : integer;
    (*

    Indicates the total number of invalid values across all the sensors.

    *)
  2. affected_sensor_count : integer;
    (*

    Indicates the number of sensors that have at least some invalid values.

    *)
}

Entity that comprises aggregated information on sensors having insufficient data.

type nonrec insufficient_sensor_data = {
  1. sensors_with_short_date_range : sensors_with_short_date_range;
    (*

    Parameter that describes the total number of sensors that have a short date range of less than 14 days of data overall.

    *)
  2. missing_complete_sensor_data : missing_complete_sensor_data;
    (*

    Parameter that describes the total number of sensors that have data completely missing for it.

    *)
}

Entity that comprises aggregated information on sensors having insufficient data.

type nonrec inline_data_schema = string
type nonrec ingested_files_summary = {
  1. discarded_files : list_of_discarded_files option;
    (*

    Indicates the number of files that were discarded. A file could be discarded because its format is invalid (for example, a jpg or pdf) or not readable.

    *)
  2. ingested_number_of_files : integer;
    (*

    Indicates the number of files that were successfully ingested.

    *)
  3. total_number_of_files : integer;
    (*

    Indicates the total number of files that were submitted for ingestion.

    *)
}

Gives statistics about how many files have been ingested, and which files have not been ingested, for a particular ingestion job.

type nonrec inference_data_import_strategy =
  1. | OVERWRITE
  2. | ADD_WHEN_EMPTY
  3. | NO_IMPORT
type nonrec import_model_version_response = {
  1. status : model_version_status option;
    (*

    The status of the ImportModelVersion operation.

    *)
  2. model_version : model_version option;
    (*

    The version of the model being created.

    *)
  3. model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the model version being created.

    *)
  4. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the model being created.

    *)
  5. model_name : model_name option;
    (*

    The name for the machine learning model.

    *)
}
type nonrec import_model_version_request = {
  1. inference_data_import_strategy : inference_data_import_strategy option;
    (*

    Indicates how to import the accumulated inference data when a model version is imported. The possible values are as follows:

    • NO_IMPORT – Don't import the data.
    • ADD_WHEN_EMPTY – Only import the data from the source model if there is no existing data in the target model.
    • OVERWRITE – Import the data from the source model and overwrite the existing data in the target model.
    *)
  2. tags : tag_list option;
    (*

    The tags associated with the machine learning model to be created.

    *)
  3. server_side_kms_key_id : name_or_arn option;
    (*

    Provides the identifier of the KMS key key used to encrypt model data by Amazon Lookout for Equipment.

    *)
  4. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.

    *)
  5. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  6. labels_input_configuration : labels_input_configuration option;
  7. dataset_name : dataset_identifier;
    (*

    The name of the dataset for the machine learning model being imported.

    *)
  8. model_name : model_name option;
    (*

    The name for the machine learning model to be created. If the model already exists, Amazon Lookout for Equipment creates a new version. If you do not specify this field, it is filled with the name of the source model.

    *)
  9. source_model_version_arn : model_version_arn;
    (*

    The Amazon Resource Name (ARN) of the model version to import.

    *)
}
type nonrec import_dataset_response = {
  1. job_id : ingestion_job_id option;
    (*

    A unique identifier for the job of importing the dataset.

    *)
  2. status : dataset_status option;
    (*

    The status of the ImportDataset operation.

    *)
  3. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset that was imported.

    *)
  4. dataset_name : dataset_name option;
    (*

    The name of the created machine learning dataset.

    *)
}
type nonrec import_dataset_request = {
  1. tags : tag_list option;
    (*

    Any tags associated with the dataset to be created.

    *)
  2. server_side_kms_key_id : name_or_arn option;
    (*

    Provides the identifier of the KMS key key used to encrypt model data by Amazon Lookout for Equipment.

    *)
  3. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  4. dataset_name : dataset_name option;
    (*

    The name of the machine learning dataset to be created. If the dataset already exists, Amazon Lookout for Equipment overwrites the existing dataset. If you don't specify this field, it is filled with the name of the source dataset.

    *)
  5. source_dataset_arn : dataset_arn;
    (*

    The Amazon Resource Name (ARN) of the dataset to import.

    *)
}
type nonrec duplicate_timestamps = {
  1. total_number_of_duplicate_timestamps : integer;
    (*

    Indicates the total number of duplicate timestamps.

    *)
}

Entity that comprises information abount duplicate timestamps in the dataset.

type nonrec describe_retraining_scheduler_response = {
  1. updated_at : timestamp option;
    (*

    Indicates the time and date at which the retraining scheduler was updated.

    *)
  2. created_at : timestamp option;
    (*

    Indicates the time and date at which the retraining scheduler was created.

    *)
  3. promote_mode : model_promote_mode option;
    (*

    Indicates how the service uses new models. In MANAGED mode, new models are used for inference if they have better performance than the current model. In MANUAL mode, the new models are not used until they are manually activated.

    *)
  4. status : retraining_scheduler_status option;
    (*

    The status of the retraining scheduler.

    *)
  5. lookback_window : lookback_window option;
    (*

    The number of past days of data used for retraining.

    *)
  6. retraining_frequency : retraining_frequency option;
    (*

    The frequency at which the model retraining is set. This follows the ISO 8601 guidelines.

    *)
  7. retraining_start_date : timestamp option;
    (*

    The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.

    *)
  8. model_arn : model_arn option;
    (*

    The ARN of the model that the retraining scheduler is attached to.

    *)
  9. model_name : model_name option;
    (*

    The name of the model that the retraining scheduler is attached to.

    *)
}
type nonrec describe_retraining_scheduler_request = {
  1. model_name : model_name;
    (*

    The name of the model that the retraining scheduler is attached to.

    *)
}
type nonrec describe_resource_policy_response = {
  1. last_modified_time : timestamp option;
    (*

    The time when the resource policy was last modified.

    *)
  2. creation_time : timestamp option;
    (*

    The time when the resource policy was created.

    *)
  3. resource_policy : policy option;
    (*

    The resource policy in a JSON-formatted string.

    *)
  4. policy_revision_id : policy_revision_id option;
    (*

    A unique identifier for a revision of the resource policy.

    *)
}
type nonrec describe_resource_policy_request = {
  1. resource_arn : resource_arn;
    (*

    The Amazon Resource Name (ARN) of the resource that is associated with the resource policy.

    *)
}
type nonrec data_pre_processing_configuration = {
  1. target_sampling_rate : target_sampling_rate option;
    (*

    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

    *)
}

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

type nonrec data_size_in_bytes = Smaws_Lib.CoreTypes.Int64.t
type nonrec auto_promotion_result =
  1. | RETRAINING_CANCELLED
  2. | RETRAINING_CUSTOMER_ERROR
  3. | RETRAINING_INTERNAL_ERROR
  4. | MODEL_NOT_PROMOTED
  5. | MODEL_PROMOTED
type nonrec auto_promotion_result_reason = string
type nonrec describe_model_version_response = {
  1. model_quality : model_quality option;
    (*

    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.

    *)
  2. model_diagnostics_results_object : s3_object option;
    (*

    The Amazon S3 output prefix for where Lookout for Equipment saves the pointwise model diagnostics for the model version.

    *)
  3. model_diagnostics_output_configuration : model_diagnostics_output_configuration option;
    (*

    The Amazon S3 location where Amazon Lookout for Equipment saves the pointwise model diagnostics for the model version.

    *)
  4. auto_promotion_result_reason : auto_promotion_result_reason option;
    (*

    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.

    *)
  5. auto_promotion_result : auto_promotion_result option;
    (*

    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.

    *)
  6. retraining_available_data_in_days : integer option;
    (*

    Indicates the number of days of data used in the most recent scheduled retraining run.

    *)
  7. prior_model_metrics : model_metrics option;
    (*

    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.

    *)
  8. imported_data_size_in_bytes : data_size_in_bytes option;
    (*

    The size in bytes of the imported data. This field appears if the model version was imported.

    *)
  9. import_job_end_time : timestamp option;
    (*

    The date and time when the import job completed. This field appears if the model version was imported.

    *)
  10. import_job_start_time : timestamp option;
    (*

    The date and time when the import job began. This field appears if the model version was imported.

    *)
  11. source_model_version_arn : model_version_arn option;
    (*

    If model version was imported, then this field is the arn of the source model version.

    *)
  12. off_condition : off_condition option;
    (*

    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.

    *)
  13. server_side_kms_key_id : kms_key_arn option;
    (*

    The identifier of the KMS key key used to encrypt model version data by Amazon Lookout for Equipment.

    *)
  14. created_at : timestamp option;
    (*

    Indicates the time and date at which the machine learning model version was created.

    *)
  15. last_updated_time : timestamp option;
    (*

    Indicates the last time the machine learning model version was updated.

    *)
  16. model_metrics : model_metrics option;
    (*

    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.

    *)
  17. failed_reason : bounded_length_string option;
    (*

    The failure message if the training of the model version failed.

    *)
  18. training_execution_end_time : timestamp option;
    (*

    The time when the training of the version completed.

    *)
  19. training_execution_start_time : timestamp option;
    (*

    The time when the training of the version began.

    *)
  20. data_pre_processing_configuration : data_pre_processing_configuration option;
  21. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of the role that was used to train the model version.

    *)
  22. evaluation_data_end_time : timestamp option;
    (*

    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.

    *)
  23. evaluation_data_start_time : timestamp option;
    (*

    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.

    *)
  24. training_data_end_time : timestamp option;
    (*

    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.

    *)
  25. training_data_start_time : timestamp option;
    (*

    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.

    *)
  26. labels_input_configuration : labels_input_configuration option;
  27. schema : inline_data_schema option;
    (*

    The schema of the data used to train the model version.

    *)
  28. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset used to train the model version.

    *)
  29. dataset_name : dataset_name option;
    (*

    The name of the dataset used to train the model version.

    *)
  30. source_type : model_version_source_type option;
    (*

    Indicates whether this model version was created by training or by importing.

    *)
  31. status : model_version_status option;
    (*

    The current status of the model version.

    *)
  32. model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the model version.

    *)
  33. model_version : model_version option;
    (*

    The version of the machine learning model.

    *)
  34. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the parent machine learning model that this version belong to.

    *)
  35. model_name : model_name option;
    (*

    The name of the machine learning model that this version belongs to.

    *)
}
type nonrec describe_model_version_request = {
  1. model_version : model_version;
    (*

    The version of the machine learning model.

    *)
  2. model_name : model_name;
    (*

    The name of the machine learning model that this version belongs to.

    *)
}
type nonrec describe_model_response = {
  1. model_quality : model_quality option;
    (*

    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.

    *)
  2. model_diagnostics_output_configuration : model_diagnostics_output_configuration option;
    (*

    Configuration information for the model's pointwise model diagnostics.

    *)
  3. retraining_scheduler_status : retraining_scheduler_status option;
    (*

    Indicates the status of the retraining scheduler.

    *)
  4. accumulated_inference_data_end_time : timestamp option;
    (*

    Indicates the end time of the inference data that has been accumulated.

    *)
  5. accumulated_inference_data_start_time : timestamp option;
    (*

    Indicates the start time of the inference data that has been accumulated.

    *)
  6. next_scheduled_retraining_start_date : timestamp option;
    (*

    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.

    *)
  7. latest_scheduled_retraining_available_data_in_days : integer option;
    (*

    Indicates the number of days of data used in the most recent scheduled retraining run.

    *)
  8. latest_scheduled_retraining_start_time : timestamp option;
    (*

    Indicates the start time of the most recent scheduled retraining run.

    *)
  9. latest_scheduled_retraining_model_version : model_version option;
    (*

    Indicates the most recent model version that was generated by retraining.

    *)
  10. latest_scheduled_retraining_status : model_version_status option;
    (*

    Indicates the status of the most recent scheduled retraining run.

    *)
  11. latest_scheduled_retraining_failed_reason : bounded_length_string option;
    (*

    If the model version was generated by retraining and the training failed, this indicates the reason for that failure.

    *)
  12. prior_model_metrics : synthesized_json_model_metrics option;
    (*

    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.

    *)
  13. previous_model_version_activated_at : timestamp option;
    (*

    The date and time when the previous active model version was activated.

    *)
  14. previous_active_model_version_arn : model_version_arn option;
    (*

    The ARN of the model version that was set as the active model version prior to the current active model version.

    *)
  15. previous_active_model_version : model_version option;
    (*

    The model version that was set as the active model version prior to the current active model version.

    *)
  16. model_version_activated_at : timestamp option;
    (*

    The date the active model version was activated.

    *)
  17. active_model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the model version used by the inference scheduler when running a scheduled inference execution.

    *)
  18. active_model_version : model_version option;
    (*

    The name of the model version used by the inference schedular when running a scheduled inference execution.

    *)
  19. import_job_end_time : timestamp option;
    (*

    The date and time when the import job was completed. This field appears if the active model version was imported.

    *)
  20. import_job_start_time : timestamp option;
    (*

    The date and time when the import job was started. This field appears if the active model version was imported.

    *)
  21. source_model_version_arn : model_version_arn option;
    (*

    The Amazon Resource Name (ARN) of the source model version. This field appears if the active model version was imported.

    *)
  22. off_condition : off_condition option;
    (*

    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.

    *)
  23. server_side_kms_key_id : kms_key_arn option;
    (*

    Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

    *)
  24. created_at : timestamp option;
    (*

    Indicates the time and date at which the machine learning model was created.

    *)
  25. last_updated_time : timestamp option;
    (*

    Indicates the last time the machine learning model was updated. The type of update is not specified.

    *)
  26. model_metrics : synthesized_json_model_metrics option;
    (*

    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.

    *)
  27. failed_reason : bounded_length_string option;
    (*

    If the training of the machine learning model failed, this indicates the reason for that failure.

    *)
  28. training_execution_end_time : timestamp option;
    (*

    Indicates the time at which the training of the machine learning model was completed.

    *)
  29. training_execution_start_time : timestamp option;
    (*

    Indicates the time at which the training of the machine learning model began.

    *)
  30. status : model_status option;
    (*

    Specifies the current status of the model being described. Status describes the status of the most recent action of the model.

    *)
  31. data_pre_processing_configuration : data_pre_processing_configuration option;
    (*

    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

    *)
  32. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source for the machine learning model being described.

    *)
  33. evaluation_data_end_time : timestamp option;
    (*

    Indicates the time reference in the dataset that was used to end the subset of evaluation data for the machine learning model.

    *)
  34. evaluation_data_start_time : timestamp option;
    (*

    Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the machine learning model.

    *)
  35. training_data_end_time : timestamp option;
    (*

    Indicates the time reference in the dataset that was used to end the subset of training data for the machine learning model.

    *)
  36. training_data_start_time : timestamp option;
    (*

    Indicates the time reference in the dataset that was used to begin the subset of training data for the machine learning model.

    *)
  37. labels_input_configuration : labels_input_configuration option;
    (*

    Specifies configuration information about the labels input, including its S3 location.

    *)
  38. schema : synthesized_json_inline_data_schema option;
    (*

    A JSON description of the data that is in each time series dataset, including names, column names, and data types.

    *)
  39. dataset_arn : dataset_arn option;
    (*

    The Amazon Resouce Name (ARN) of the dataset used to create the machine learning model being described.

    *)
  40. dataset_name : dataset_name option;
    (*

    The name of the dataset being used by the machine learning being described.

    *)
  41. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model being described.

    *)
  42. model_name : model_name option;
    (*

    The name of the machine learning model being described.

    *)
}
type nonrec describe_model_request = {
  1. model_name : model_name;
    (*

    The name of the machine learning model to be described.

    *)
}
type nonrec comments = string
type nonrec describe_label_response = {
  1. created_at : timestamp option;
    (*

    The time at which the label was created.

    *)
  2. equipment : equipment option;
    (*

    Indicates that a label pertains to a particular piece of equipment.

    *)
  3. notes : comments option;
    (*

    Metadata providing additional information about the label.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  4. fault_code : fault_code option;
    (*

    Indicates the type of anomaly associated with the label.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  5. rating : label_rating option;
    (*

    Indicates whether a labeled event represents an anomaly.

    *)
  6. end_time : timestamp option;
    (*

    The end time of the requested label.

    *)
  7. start_time : timestamp option;
    (*

    The start time of the requested label.

    *)
  8. label_id : label_id option;
    (*

    The ID of the requested label.

    *)
  9. label_group_arn : label_group_arn option;
    (*

    The Amazon Resource Name (ARN) of the requested label group.

    *)
  10. label_group_name : label_group_name option;
    (*

    The name of the requested label group.

    *)
}
type nonrec describe_label_request = {
  1. label_id : label_id;
    (*

    Returns the ID of the label.

    *)
  2. label_group_name : label_group_name;
    (*

    Returns the name of the group containing the label.

    *)
}
type nonrec describe_label_group_response = {
  1. updated_at : timestamp option;
    (*

    The time at which the label group was updated.

    *)
  2. created_at : timestamp option;
    (*

    The time at which the label group was created.

    *)
  3. fault_codes : fault_codes option;
    (*

    Codes indicating the type of anomaly associated with the labels in the lagbel group.

    *)
  4. label_group_arn : label_group_arn option;
    (*

    The Amazon Resource Name (ARN) of the label group.

    *)
  5. label_group_name : label_group_name option;
    (*

    The name of the label group.

    *)
}
type nonrec describe_label_group_request = {
  1. label_group_name : label_group_name;
    (*

    Returns the name of the label group.

    *)
}
type nonrec describe_inference_scheduler_response = {
  1. latest_inference_result : latest_inference_result option;
    (*

    Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found).

    *)
  2. server_side_kms_key_id : kms_key_arn option;
    (*

    Provides the identifier of the KMS key used to encrypt inference scheduler data by Amazon Lookout for Equipment.

    *)
  3. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source for the inference scheduler being described.

    *)
  4. data_output_configuration : inference_output_configuration option;
    (*

    Specifies information for the output results for the inference scheduler, including the output S3 location.

    *)
  5. data_input_configuration : inference_input_configuration option;
    (*

    Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location.

    *)
  6. updated_at : timestamp option;
    (*

    Specifies the time at which the inference scheduler was last updated, if it was.

    *)
  7. created_at : timestamp option;
    (*

    Specifies the time at which the inference scheduler was created.

    *)
  8. data_upload_frequency : data_upload_frequency option;
    (*

    Specifies how often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes.

    *)
  9. data_delay_offset_in_minutes : data_delay_offset_in_minutes option;
    (*

    A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if you select an offset delay time of five minutes, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data.

    *)
  10. status : inference_scheduler_status option;
    (*

    Indicates the status of the inference scheduler.

    *)
  11. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler being described.

    *)
  12. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of the inference scheduler being described.

    *)
  13. model_name : model_name option;
    (*

    The name of the machine learning model of the inference scheduler being described.

    *)
  14. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the machine learning model of the inference scheduler being described.

    *)
}
type nonrec describe_inference_scheduler_request = {
  1. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler being described.

    *)
}
type nonrec data_quality_summary = {
  1. duplicate_timestamps : duplicate_timestamps;
    (*

    Parameter that gives information about duplicate timestamps in the input data.

    *)
  2. unsupported_timestamps : unsupported_timestamps;
    (*

    Parameter that gives information about unsupported timestamps in the input data.

    *)
  3. invalid_sensor_data : invalid_sensor_data;
    (*

    Parameter that gives information about data that is invalid over all the sensors in the input data.

    *)
  4. missing_sensor_data : missing_sensor_data;
    (*

    Parameter that gives information about data that is missing over all the sensors in the input data.

    *)
  5. insufficient_sensor_data : insufficient_sensor_data;
    (*

    Parameter that gives information about insufficient data for sensors in the dataset. This includes information about those sensors that have complete data missing and those with a short date range.

    *)
}

DataQualitySummary gives aggregated statistics over all the sensors about a completed ingestion job. It primarily gives more information about statistics over different incorrect data like MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, DuplicateTimeStamps.

type nonrec describe_dataset_response = {
  1. source_dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the source dataset from which the current data being described was imported from.

    *)
  2. data_end_time : timestamp option;
    (*

    Indicates the latest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset.

    *)
  3. data_start_time : timestamp option;
    (*

    Indicates the earliest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset.

    *)
  4. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of the IAM role that you are using for this the data ingestion job.

    *)
  5. ingested_files_summary : ingested_files_summary option;
    (*

    IngestedFilesSummary associated with the given dataset for the latest successful associated ingestion job id.

    *)
  6. data_quality_summary : data_quality_summary option;
    (*

    Gives statistics associated with the given dataset for the latest successful associated ingestion job id. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps.

    *)
  7. ingestion_input_configuration : ingestion_input_configuration option;
    (*

    Specifies the S3 location configuration for the data input for the data ingestion job.

    *)
  8. server_side_kms_key_id : kms_key_arn option;
    (*

    Provides the identifier of the KMS key used to encrypt dataset data by Amazon Lookout for Equipment.

    *)
  9. schema : synthesized_json_inline_data_schema option;
    (*

    A JSON description of the data that is in each time series dataset, including names, column names, and data types.

    *)
  10. status : dataset_status option;
    (*

    Indicates the status of the dataset.

    *)
  11. last_updated_at : timestamp option;
    (*

    Specifies the time the dataset was last updated, if it was.

    *)
  12. created_at : timestamp option;
    (*

    Specifies the time the dataset was created in Lookout for Equipment.

    *)
  13. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset being described.

    *)
  14. dataset_name : dataset_name option;
    (*

    The name of the dataset being described.

    *)
}
type nonrec describe_dataset_request = {
  1. dataset_name : dataset_identifier;
    (*

    The name of the dataset to be described.

    *)
}
type nonrec describe_data_ingestion_job_response = {
  1. source_dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the source dataset from which the data used for the data ingestion job was imported from.

    *)
  2. data_end_time : timestamp option;
    (*

    Indicates the latest timestamp corresponding to data that was successfully ingested during this specific ingestion job.

    *)
  3. data_start_time : timestamp option;
    (*

    Indicates the earliest timestamp corresponding to data that was successfully ingested during this specific ingestion job.

    *)
  4. ingested_data_size : data_size_in_bytes option;
    (*

    Indicates the size of the ingested dataset.

    *)
  5. status_detail : bounded_length_string option;
    (*

    Provides details about status of the ingestion job that is currently in progress.

    *)
  6. ingested_files_summary : ingested_files_summary option;
  7. data_quality_summary : data_quality_summary option;
    (*

    Gives statistics about a completed ingestion job. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps.

    *)
  8. failed_reason : bounded_length_string option;
    (*

    Specifies the reason for failure when a data ingestion job has failed.

    *)
  9. status : ingestion_job_status option;
    (*

    Indicates the status of the DataIngestionJob operation.

    *)
  10. created_at : timestamp option;
    (*

    The time at which the data ingestion job was created.

    *)
  11. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of an IAM role with permission to access the data source being ingested.

    *)
  12. ingestion_input_configuration : ingestion_input_configuration option;
    (*

    Specifies the S3 location configuration for the data input for the data ingestion job.

    *)
  13. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset being used in the data ingestion job.

    *)
  14. job_id : ingestion_job_id option;
    (*

    Indicates the job ID of the data ingestion job.

    *)
}
type nonrec describe_data_ingestion_job_request = {
  1. job_id : ingestion_job_id;
    (*

    The job ID of the data ingestion job.

    *)
}
type nonrec delete_retraining_scheduler_request = {
  1. model_name : model_name;
    (*

    The name of the model whose retraining scheduler you want to delete.

    *)
}
type nonrec delete_resource_policy_request = {
  1. resource_arn : resource_arn;
    (*

    The Amazon Resource Name (ARN) of the resource for which the resource policy should be deleted.

    *)
}
type nonrec delete_model_request = {
  1. model_name : model_name;
    (*

    The name of the machine learning model to be deleted.

    *)
}
type nonrec delete_label_request = {
  1. label_id : label_id;
    (*

    The ID of the label that you want to delete.

    *)
  2. label_group_name : label_group_name;
    (*

    The name of the label group that contains the label that you want to delete. Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
}
type nonrec delete_label_group_request = {
  1. label_group_name : label_group_name;
    (*

    The name of the label group that you want to delete. Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
}
type nonrec delete_inference_scheduler_request = {
  1. inference_scheduler_name : inference_scheduler_identifier;
    (*

    The name of the inference scheduler to be deleted.

    *)
}
type nonrec delete_dataset_request = {
  1. dataset_name : dataset_identifier;
    (*

    The name of the dataset to be deleted.

    *)
}
type nonrec dataset_schema = {
  1. inline_data_schema : synthesized_json_inline_data_schema option;
    (*

    The data schema used within the given dataset.

    *)
}

Provides information about the data schema used with the given dataset.

type nonrec create_retraining_scheduler_response = {
  1. status : retraining_scheduler_status option;
    (*

    The status of the retraining scheduler.

    *)
  2. model_arn : model_arn option;
    (*

    The ARN of the model that you added the retraining scheduler to.

    *)
  3. model_name : model_name option;
    (*

    The name of the model that you added the retraining scheduler to.

    *)
}
type nonrec create_retraining_scheduler_request = {
  1. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  2. promote_mode : model_promote_mode option;
    (*

    Indicates how the service will use new models. In MANAGED mode, new models will automatically be used for inference if they have better performance than the current model. In MANUAL mode, the new models will not be used until they are manually activated.

    *)
  3. lookback_window : lookback_window;
    (*

    The number of past days of data that will be used for retraining.

    *)
  4. retraining_frequency : retraining_frequency;
    (*

    This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:

    • P3M15D – Every 3 months and 15 days
    • P2M – Every 2 months
    • P150D – Every 150 days
    *)
  5. retraining_start_date : timestamp option;
    (*

    The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.

    *)
  6. model_name : model_name;
    (*

    The name of the model to add the retraining scheduler to.

    *)
}
type nonrec create_model_response = {
  1. status : model_status option;
    (*

    Indicates the status of the CreateModel operation.

    *)
  2. model_arn : model_arn option;
    (*

    The Amazon Resource Name (ARN) of the model being created.

    *)
}
type nonrec create_model_request = {
  1. model_diagnostics_output_configuration : model_diagnostics_output_configuration option;
    (*

    The Amazon S3 location where you want Amazon Lookout for Equipment to save the pointwise model diagnostics. You must also specify the RoleArn request parameter.

    *)
  2. off_condition : off_condition option;
    (*

    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.

    *)
  3. tags : tag_list option;
    (*

    Any tags associated with the machine learning model being created.

    *)
  4. server_side_kms_key_id : name_or_arn option;
    (*

    Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.

    *)
  5. data_pre_processing_configuration : data_pre_processing_configuration option;
    (*

    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

    *)
  6. role_arn : iam_role_arn option;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.

    *)
  7. evaluation_data_end_time : timestamp option;
    (*

    Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the machine learning model.

    *)
  8. evaluation_data_start_time : timestamp option;
    (*

    Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the machine learning model.

    *)
  9. training_data_end_time : timestamp option;
    (*

    Indicates the time reference in the dataset that should be used to end the subset of training data for the machine learning model.

    *)
  10. training_data_start_time : timestamp option;
    (*

    Indicates the time reference in the dataset that should be used to begin the subset of training data for the machine learning model.

    *)
  11. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  12. labels_input_configuration : labels_input_configuration option;
    (*

    The input configuration for the labels being used for the machine learning model that's being created.

    *)
  13. dataset_schema : dataset_schema option;
    (*

    The data schema for the machine learning model being created.

    *)
  14. dataset_name : dataset_identifier;
    (*

    The name of the dataset for the machine learning model being created.

    *)
  15. model_name : model_name;
    (*

    The name for the machine learning model to be created.

    *)
}
type nonrec create_label_response = {
  1. label_id : label_id option;
    (*

    The ID of the label that you have created.

    *)
}
type nonrec create_label_request = {
  1. client_token : idempotence_token;
    (*

    A unique identifier for the request to create a label. If you do not set the client request token, Lookout for Equipment generates one.

    *)
  2. equipment : equipment option;
    (*

    Indicates that a label pertains to a particular piece of equipment.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  3. notes : comments option;
    (*

    Metadata providing additional information about the label.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  4. fault_code : fault_code option;
    (*

    Provides additional information about the label. The fault code must be defined in the FaultCodes attribute of the label group.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  5. rating : label_rating;
    (*

    Indicates whether a labeled event represents an anomaly.

    *)
  6. end_time : timestamp;
    (*

    The end time of the labeled event.

    *)
  7. start_time : timestamp;
    (*

    The start time of the labeled event.

    *)
  8. label_group_name : label_group_name;
    (*

    The name of a group of labels.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
}
type nonrec create_label_group_response = {
  1. label_group_arn : label_group_arn option;
    (*

    The Amazon Resource Name (ARN) of the label group that you have created.

    *)
  2. label_group_name : label_group_name option;
    (*

    The name of the label group that you have created. Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
}
type nonrec create_label_group_request = {
  1. tags : tag_list option;
    (*

    Tags that provide metadata about the label group you are creating.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  2. client_token : idempotence_token;
    (*

    A unique identifier for the request to create a label group. If you do not set the client request token, Lookout for Equipment generates one.

    *)
  3. fault_codes : fault_codes option;
    (*

    The acceptable fault codes (indicating the type of anomaly associated with the label) that can be used with this label group.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
  4. label_group_name : label_group_name;
    (*

    Names a group of labels.

    Data in this field will be retained for service usage. Follow best practices for the security of your data.

    *)
}
type nonrec create_inference_scheduler_response = {
  1. model_quality : model_quality option;
    (*

    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.

    *)
  2. status : inference_scheduler_status option;
    (*

    Indicates the status of the CreateInferenceScheduler operation.

    *)
  3. inference_scheduler_name : inference_scheduler_name option;
    (*

    The name of inference scheduler being created.

    *)
  4. inference_scheduler_arn : inference_scheduler_arn option;
    (*

    The Amazon Resource Name (ARN) of the inference scheduler being created.

    *)
}
type nonrec create_inference_scheduler_request = {
  1. tags : tag_list option;
    (*

    Any tags associated with the inference scheduler.

    *)
  2. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  3. server_side_kms_key_id : name_or_arn option;
    (*

    Provides the identifier of the KMS key used to encrypt inference scheduler data by Amazon Lookout for Equipment.

    *)
  4. role_arn : iam_role_arn;
    (*

    The Amazon Resource Name (ARN) of a role with permission to access the data source being used for the inference.

    *)
  5. data_output_configuration : inference_output_configuration;
    (*

    Specifies configuration information for the output results for the inference scheduler, including the S3 location for the output.

    *)
  6. data_input_configuration : inference_input_configuration;
    (*

    Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location.

    *)
  7. data_upload_frequency : data_upload_frequency;
    (*

    How often data is uploaded to the source Amazon S3 bucket for the input data. The value chosen is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment runs inference on your data.

    For more information, see Understanding the inference process.

    *)
  8. data_delay_offset_in_minutes : data_delay_offset_in_minutes option;
    (*

    The interval (in minutes) of planned delay at the start of each inference segment. For example, if inference is set to run every ten minutes, the delay is set to five minutes and the time is 09:08. The inference scheduler will wake up at the configured interval (which, without a delay configured, would be 09:10) plus the additional five minute delay time (so 09:15) to check your Amazon S3 bucket. The delay provides a buffer for you to upload data at the same frequency, so that you don't have to stop and restart the scheduler when uploading new data.

    For more information, see Understanding the inference process.

    *)
  9. inference_scheduler_name : inference_scheduler_name;
    (*

    The name of the inference scheduler being created.

    *)
  10. model_name : model_name;
    (*

    The name of the previously trained machine learning model being used to create the inference scheduler.

    *)
}
type nonrec create_dataset_response = {
  1. status : dataset_status option;
    (*

    Indicates the status of the CreateDataset operation.

    *)
  2. dataset_arn : dataset_arn option;
    (*

    The Amazon Resource Name (ARN) of the dataset being created.

    *)
  3. dataset_name : dataset_name option;
    (*

    The name of the dataset being created.

    *)
}
type nonrec create_dataset_request = {
  1. tags : tag_list option;
    (*

    Any tags associated with the ingested data described in the dataset.

    *)
  2. client_token : idempotence_token;
    (*

    A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.

    *)
  3. server_side_kms_key_id : name_or_arn option;
    (*

    Provides the identifier of the KMS key used to encrypt dataset data by Amazon Lookout for Equipment.

    *)
  4. dataset_schema : dataset_schema option;
    (*

    A JSON description of the data that is in each time series dataset, including names, column names, and data types.

    *)
  5. dataset_name : dataset_name;
    (*

    The name of the dataset being created.

    *)
}