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AWS for Query.Farm

aws.lookoutequipment.describe_model

Example SQL Queries

SELECT * FROM
aws.lookoutequipment.describe_model
WHERE
"model_name" = 'VALUE';

Description

Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on.

Table Definition

Column NameColumn Data Type
model_name Required Input Column

The name of the machine learning model being described. 

VARCHAR
_aws_profile Input Column

The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role.

STRUCT(
    "type" VARCHAR,
    "name" VARCHAR,
    "account_id" VARCHAR,
    "via_profile_name" VARCHAR,
    "assumed_role_arn" VARCHAR,
    "organization" STRUCT(
        "account_name" VARCHAR,
        "id" VARCHAR,
        "tags" STRUCT(
            "key" VARCHAR,
            "value" VARCHAR
        )[],
        "master_account" STRUCT(
            "id" VARCHAR,
            "email" VARCHAR
        ),
        "parents" STRUCT(
            "type" VARCHAR,
            "id" VARCHAR,
            "name" VARCHAR,
            "tags" STRUCT(
                "key" VARCHAR,
                "value" VARCHAR
            )[]
        )[]
    )
)
Show child fields
_aws_profile.account_id

The AWS account id

_aws_profile.assumed_role_arn

The ARN of the assumed role

_aws_profile.name

The unique name of the profile.

_aws_profile.organization

Information about this profile's membership in the AWS organization.

Show child fields
_aws_profile.organization.account_name

The name of account speciifed by the organization

_aws_profile.organization.id

The organization id

_aws_profile.organization.master_account
Show child fields
_aws_profile.organization.master_account.email

The organization master account email address

_aws_profile.organization.master_account.id

The organization master account id

_aws_profile.organization.parents[]
Show child fields
_aws_profile.organization.parents[].id

The id of the parent

_aws_profile.organization.parents[].name

The name of the parent

_aws_profile.organization.parents[].tags[]
Show child fields
_aws_profile.organization.parents[].tags[].key
_aws_profile.organization.parents[].tags[].value
_aws_profile.organization.parents[].type

The type of parent can be an organization unit or a root

_aws_profile.organization.tags[]
Show child fields
_aws_profile.organization.tags[].key
_aws_profile.organization.tags[].value
_aws_profile.type

The type of profile, either 'credentials' or 'assumed_role'

_aws_profile.via_profile_name

This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.

_aws_region Input Column

The AWS region to use.

VARCHAR
accumulated_inference_data_end_time

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

TIMESTAMP_S
accumulated_inference_data_start_time

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

TIMESTAMP_S
active_model_version

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

BIGINT
active_model_version_arn

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

VARCHAR
created_at

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

TIMESTAMP_S
data_pre_processing_configuration

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 

STRUCT(
    "target_sampling_rate" VARCHAR
)
Show child fields
data_pre_processing_configuration.target_sampling_rate

The sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.

When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H

dataset_arn

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

VARCHAR
dataset_name

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

VARCHAR
evaluation_data_end_time

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

TIMESTAMP_S
evaluation_data_start_time

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

TIMESTAMP_S
failed_reason

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

VARCHAR
import_job_end_time

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

TIMESTAMP_S
import_job_start_time

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

TIMESTAMP_S
labels_input_configuration

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

STRUCT(
    "s3_input_configuration" STRUCT(
        "bucket" VARCHAR,
        "prefix" VARCHAR
    ),
    "label_group_name" VARCHAR
)
Show child fields
labels_input_configuration.label_group_name

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

labels_input_configuration.s3_input_configuration

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

Show child fields
labels_input_configuration.s3_input_configuration.bucket

The name of the S3 bucket holding the label data.

labels_input_configuration.s3_input_configuration.prefix

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

last_updated_time

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

TIMESTAMP_S
latest_scheduled_retraining_available_data_in_days

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

BIGINT
latest_scheduled_retraining_failed_reason

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

VARCHAR
latest_scheduled_retraining_model_version

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

BIGINT
latest_scheduled_retraining_start_time

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

TIMESTAMP_S
latest_scheduled_retraining_status

Indicates the status of the most recent scheduled retraining run. 

VARCHAR
model_arn

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

VARCHAR
model_diagnostics_output_configuration

Configuration information for the model's pointwise model diagnostics.

STRUCT(
    "s3_output_configuration" STRUCT(
        "bucket" VARCHAR,
        "prefix" VARCHAR
    ),
    "kms_key_id" VARCHAR
)
Show child fields
model_diagnostics_output_configuration.kms_key_id

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

model_diagnostics_output_configuration.s3_output_configuration

The Amazon S3 location for the pointwise model diagnostics.

Show child fields
model_diagnostics_output_configuration.s3_output_configuration.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.

model_diagnostics_output_configuration.s3_output_configuration.prefix

The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. (bucket).

When you call CreateModel or UpdateModel, specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz.

When you call DescribeModel or DescribeModelVersion, prefix contains the file path and filename of the model evaluation file.

model_metrics

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. 

VARCHAR
model_quality

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.

VARCHAR
model_version_activated_at

The date the active model version was activated.

TIMESTAMP_S
next_scheduled_retraining_start_date

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.

TIMESTAMP_S
off_condition

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.

VARCHAR
previous_active_model_version

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

BIGINT
previous_active_model_version_arn

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

VARCHAR
previous_model_version_activated_at

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

TIMESTAMP_S
prior_model_metrics

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.

VARCHAR
retraining_scheduler_status

Indicates the status of the retraining scheduler. 

VARCHAR
role_arn

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

VARCHAR
schema

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

VARCHAR
server_side_kms_key_id

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

VARCHAR
source_model_version_arn

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

VARCHAR
status

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

VARCHAR
training_data_end_time

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

TIMESTAMP_S
training_data_start_time

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

TIMESTAMP_S
training_execution_end_time

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

TIMESTAMP_S
training_execution_start_time

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

TIMESTAMP_S