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AWS for Query.Farm

aws.lookoutequipment.list_models

Example SQL Queries

SELECT * FROM
aws.lookoutequipment.list_models;

Description

Generates a list of all models in the account, including model name and ARN, dataset, and status.

Table Definition

Column NameColumn Data Type
dataset_name_begins_with Input Column

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

VARCHAR
max_results Input Column

Specifies the maximum number of machine learning models to list. 

BIGINT
model_name_begins_with Input Column

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

VARCHAR
next_token Input Column

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

VARCHAR
status Input Column

The status of the machine learning model. 

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
model_summaries

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

STRUCT(
    "model_name" VARCHAR,
    "model_arn" VARCHAR,
    "dataset_name" VARCHAR,
    "dataset_arn" VARCHAR,
    "status" VARCHAR,
    "created_at" TIMESTAMP_S,
    "active_model_version" BIGINT,
    "active_model_version_arn" VARCHAR,
    "latest_scheduled_retraining_status" VARCHAR,
    "latest_scheduled_retraining_model_version" BIGINT,
    "latest_scheduled_retraining_start_time" TIMESTAMP_S,
    "next_scheduled_retraining_start_date" TIMESTAMP_S,
    "retraining_scheduler_status" VARCHAR,
    "model_diagnostics_output_configuration" STRUCT(
        "s3_output_configuration" STRUCT(
            "bucket" VARCHAR,
            "prefix" VARCHAR
        ),
        "kms_key_id" VARCHAR
    ),
    "model_quality" VARCHAR
)[]
Show child fields
model_summaries[]
Show child fields
model_summaries[].active_model_version

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

model_summaries[].active_model_version_arn

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.

model_summaries[].created_at

The time at which the specific model was created.

model_summaries[].dataset_arn

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

model_summaries[].dataset_name

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

model_summaries[].latest_scheduled_retraining_model_version

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

model_summaries[].latest_scheduled_retraining_start_time

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

model_summaries[].latest_scheduled_retraining_status

Indicates the status of the most recent scheduled retraining run.

model_summaries[].model_arn

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

model_summaries[].model_diagnostics_output_configuration

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

Show child fields
model_summaries[].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_summaries[].model_diagnostics_output_configuration.s3_output_configuration

The Amazon S3 location for the pointwise model diagnostics.

Show child fields
model_summaries[].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_summaries[].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_summaries[].model_name

The name of the machine learning model.

model_summaries[].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.

model_summaries[].next_scheduled_retraining_start_date

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.

model_summaries[].retraining_scheduler_status

Indicates the status of the retraining scheduler.

model_summaries[].status

Indicates the status of the machine learning model.