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AWS for Query.Farm

aws.sagemaker.describe_monitoring_schedule

Example SQL Queries

SELECT * FROM
aws.sagemaker.describe_monitoring_schedule
WHERE
"monitoring_schedule_name" = 'VALUE';

Description

Describes the schedule for a monitoring job.

Table Definition

Column NameColumn Data Type
monitoring_schedule_name Required Input Column

Name of the monitoring schedule.

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.

creation_time

The time at which the monitoring job was created.

TIMESTAMP_S
endpoint_name

The name of the endpoint for the monitoring job.

VARCHAR
failure_reason

A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.

VARCHAR
last_modified_time

The time at which the monitoring job was last modified.

TIMESTAMP_S
last_monitoring_execution_summary

Describes metadata on the last execution to run, if there was one.

STRUCT(
    "monitoring_schedule_name" VARCHAR,
    "scheduled_time" TIMESTAMP_S,
    "creation_time" TIMESTAMP_S,
    "last_modified_time" TIMESTAMP_S,
    "monitoring_execution_status" VARCHAR,
    "processing_job_arn" VARCHAR,
    "endpoint_name" VARCHAR,
    "failure_reason" VARCHAR,
    "monitoring_job_definition_name" VARCHAR,
    "monitoring_type" VARCHAR
)
Show child fields
last_monitoring_execution_summary.creation_time

The time at which the monitoring job was created.

last_monitoring_execution_summary.endpoint_name

The name of the endpoint used to run the monitoring job.

last_monitoring_execution_summary.failure_reason

Contains the reason a monitoring job failed, if it failed.

last_monitoring_execution_summary.last_modified_time

A timestamp that indicates the last time the monitoring job was modified.

last_monitoring_execution_summary.monitoring_execution_status

The status of the monitoring job.

last_monitoring_execution_summary.monitoring_job_definition_name

The name of the monitoring job.

last_monitoring_execution_summary.monitoring_schedule_name

The name of the monitoring schedule.

last_monitoring_execution_summary.monitoring_type

The type of the monitoring job.

last_monitoring_execution_summary.processing_job_arn

The Amazon Resource Name (ARN) of the monitoring job.

last_monitoring_execution_summary.scheduled_time

The time the monitoring job was scheduled.

monitoring_schedule_arn

The Amazon Resource Name (ARN) of the monitoring schedule.

VARCHAR
monitoring_schedule_config

The configuration object that specifies the monitoring schedule and defines the monitoring job.

STRUCT(
    "schedule_config" STRUCT(
        "schedule_expression" VARCHAR,
        "data_analysis_start_time" VARCHAR,
        "data_analysis_end_time" VARCHAR
    ),
    "monitoring_job_definition" STRUCT(
        "baseline_config" STRUCT(
            "baselining_job_name" VARCHAR,
            "constraints_resource" STRUCT(
                "s3_uri" VARCHAR
            ),
            "statistics_resource" STRUCT(
                "s3_uri" VARCHAR
            )
        ),
        "monitoring_inputs" STRUCT(
            "endpoint_input" STRUCT(
                "endpoint_name" VARCHAR,
                "local_path" VARCHAR,
                "s3_input_mode" VARCHAR,
                "s3_data_distribution_type" VARCHAR,
                "features_attribute" VARCHAR,
                "inference_attribute" VARCHAR,
                "probability_attribute" VARCHAR,
                "probability_threshold_attribute" DOUBLE,
                "start_time_offset" VARCHAR,
                "end_time_offset" VARCHAR,
                "exclude_features_attribute" VARCHAR
            ),
            "batch_transform_input" STRUCT(
                "data_captured_destination_s3_uri" VARCHAR,
                "dataset_format" STRUCT(
                    "csv" STRUCT(
                        "header" BOOLEAN
                    ),
                    "json" STRUCT(
                        "line" BOOLEAN
                    ),
                    "parquet" BOOLEAN
                ),
                "local_path" VARCHAR,
                "s3_input_mode" VARCHAR,
                "s3_data_distribution_type" VARCHAR,
                "features_attribute" VARCHAR,
                "inference_attribute" VARCHAR,
                "probability_attribute" VARCHAR,
                "probability_threshold_attribute" DOUBLE,
                "start_time_offset" VARCHAR,
                "end_time_offset" VARCHAR,
                "exclude_features_attribute" VARCHAR
            )
        )[],
        "monitoring_output_config" STRUCT(
            "monitoring_outputs" STRUCT(
                "s3_output" STRUCT(
                    "s3_uri" VARCHAR,
                    "local_path" VARCHAR,
                    "s3_upload_mode" VARCHAR
                )
            )[],
            "kms_key_id" VARCHAR
        ),
        "monitoring_resources" STRUCT(
            "cluster_config" STRUCT(
                "instance_count" BIGINT,
                "instance_type" VARCHAR,
                "volume_size_in_gb" BIGINT,
                "volume_kms_key_id" VARCHAR
            )
        ),
        "monitoring_app_specification" STRUCT(
            "image_uri" VARCHAR,
            "container_entrypoint" VARCHAR[],
            "container_arguments" VARCHAR[],
            "record_preprocessor_source_uri" VARCHAR,
            "post_analytics_processor_source_uri" VARCHAR
        ),
        "stopping_condition" STRUCT(
            "max_runtime_in_seconds" BIGINT
        ),
        "environment" MAP(VARCHAR, VARCHAR),
        "network_config" STRUCT(
            "enable_inter_container_traffic_encryption" BOOLEAN,
            "enable_network_isolation" BOOLEAN,
            "vpc_config" STRUCT(
                "security_group_ids" VARCHAR[],
                "subnets" VARCHAR[]
            )
        ),
        "role_arn" VARCHAR
    ),
    "monitoring_job_definition_name" VARCHAR,
    "monitoring_type" VARCHAR
)
Show child fields
monitoring_schedule_config.monitoring_job_definition

Defines the monitoring job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.baseline_config

Baseline configuration used to validate that the data conforms to the specified constraints and statistics

Show child fields
monitoring_schedule_config.monitoring_job_definition.baseline_config.baselining_job_name

The name of the job that performs baselining for the monitoring job.

monitoring_schedule_config.monitoring_job_definition.baseline_config.constraints_resource

The baseline constraint file in Amazon S3 that the current monitoring job should validated against.

Show child fields
monitoring_schedule_config.monitoring_job_definition.baseline_config.constraints_resource.s3_uri

The Amazon S3 URI for the constraints resource.

monitoring_schedule_config.monitoring_job_definition.baseline_config.statistics_resource

The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.

Show child fields
monitoring_schedule_config.monitoring_job_definition.baseline_config.statistics_resource.s3_uri

The Amazon S3 URI for the statistics resource.

monitoring_schedule_config.monitoring_job_definition.environment

Sets the environment variables in the Docker container.

monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification

Configures the monitoring job to run a specified Docker container image.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_arguments[]
monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.container_entrypoint[]
monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.image_uri

The container image to be run by the monitoring job.

monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.post_analytics_processor_source_uri

An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.

monitoring_schedule_config.monitoring_job_definition.monitoring_app_specification.record_preprocessor_source_uri

An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[]
Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input

Input object for the batch transform job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.data_captured_destination_s3_uri

The Amazon S3 location being used to capture the data.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.dataset_format

The dataset format for your batch transform job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.dataset_format.csv

The CSV dataset used in the monitoring job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.dataset_format.csv.header

Indicates if the CSV data has a header.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.dataset_format.json

The JSON dataset used in the monitoring job

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.dataset_format.json.line

Indicates if the file should be read as a JSON object per line.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.dataset_format.parquet

The Parquet dataset used in the monitoring job

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.end_time_offset

If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.exclude_features_attribute

The attributes of the input data to exclude from the analysis.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.features_attribute

The attributes of the input data that are the input features.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.inference_attribute

The attribute of the input data that represents the ground truth label.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.local_path

Path to the filesystem where the batch transform data is available to the container.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.probability_attribute

In a classification problem, the attribute that represents the class probability.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.probability_threshold_attribute

The threshold for the class probability to be evaluated as a positive result.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.s3_data_distribution_type

Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.s3_input_mode

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].batch_transform_input.start_time_offset

If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input

The endpoint for a monitoring job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.end_time_offset

If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.endpoint_name

An endpoint in customer's account which has enabled DataCaptureConfig enabled.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.exclude_features_attribute

The attributes of the input data to exclude from the analysis.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.features_attribute

The attributes of the input data that are the input features.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.inference_attribute

The attribute of the input data that represents the ground truth label.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.local_path

Path to the filesystem where the endpoint data is available to the container.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.probability_attribute

In a classification problem, the attribute that represents the class probability.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.probability_threshold_attribute

The threshold for the class probability to be evaluated as a positive result.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.s3_data_distribution_type

Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.s3_input_mode

Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.

monitoring_schedule_config.monitoring_job_definition.monitoring_inputs[].endpoint_input.start_time_offset

If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.

monitoring_schedule_config.monitoring_job_definition.monitoring_output_config

The array of outputs from the monitoring job to be uploaded to Amazon S3.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.kms_key_id

The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[]
Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[].s3_output

The Amazon S3 storage location where the results of a monitoring job are saved.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[].s3_output.local_path

The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.

monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[].s3_output.s3_upload_mode

Whether to upload the results of the monitoring job continuously or after the job completes.

monitoring_schedule_config.monitoring_job_definition.monitoring_output_config.monitoring_outputs[].s3_output.s3_uri

A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.

monitoring_schedule_config.monitoring_job_definition.monitoring_resources

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config

The configuration for the cluster resources used to run the processing job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_count

The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.

monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.instance_type

The ML compute instance type for the processing job.

monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_kms_key_id

The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.

monitoring_schedule_config.monitoring_job_definition.monitoring_resources.cluster_config.volume_size_in_gb

The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.

monitoring_schedule_config.monitoring_job_definition.network_config

Specifies networking options for an monitoring job.

Show child fields
monitoring_schedule_config.monitoring_job_definition.network_config.enable_inter_container_traffic_encryption

Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.

monitoring_schedule_config.monitoring_job_definition.network_config.enable_network_isolation

Whether to allow inbound and outbound network calls to and from the containers used for the processing job.

monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

Show child fields
monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.security_group_ids[]
monitoring_schedule_config.monitoring_job_definition.network_config.vpc_config.subnets[]
monitoring_schedule_config.monitoring_job_definition.role_arn

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

monitoring_schedule_config.monitoring_job_definition.stopping_condition

Specifies a time limit for how long the monitoring job is allowed to run.

Show child fields
monitoring_schedule_config.monitoring_job_definition.stopping_condition.max_runtime_in_seconds

The maximum runtime allowed in seconds.

The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds.

monitoring_schedule_config.monitoring_job_definition_name

The name of the monitoring job definition to schedule.

monitoring_schedule_config.monitoring_type

The type of the monitoring job definition to schedule.

monitoring_schedule_config.schedule_config

Configures the monitoring schedule.

Show child fields
monitoring_schedule_config.schedule_config.data_analysis_end_time

Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H".

The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter.

If you set ScheduleExpression to NOW, this parameter is required.

monitoring_schedule_config.schedule_config.data_analysis_start_time

Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H".

The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter.

If you set ScheduleExpression to NOW, this parameter is required.

monitoring_schedule_config.schedule_config.schedule_expression

A cron expression that describes details about the monitoring schedule.

The supported cron expressions are:

  • If you want to set the job to start every hour, use the following:

    Hourly: cron(0 * ? * * *)

  • If you want to start the job daily:

    cron(0 [00-23] ? * * *)

  • If you want to run the job one time, immediately, use the following keyword:

    NOW

For example, the following are valid cron expressions:

  • Daily at noon UTC: cron(0 12 ? * * *)

  • Daily at midnight UTC: cron(0 0 ? * * *)

To support running every 6, 12 hours, the following are also supported:

cron(0 [00-23]/[01-24] ? * * *)

For example, the following are valid cron expressions:

  • Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)

  • Every two hours starting at midnight: cron(0 0/2 ? * * *)

  • Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.

  • We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.

You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.

monitoring_schedule_status

The status of an monitoring job.

VARCHAR
monitoring_type

The type of the monitoring job that this schedule runs. This is one of the following values.

  • DATA_QUALITY - The schedule is for a data quality monitoring job.

  • MODEL_QUALITY - The schedule is for a model quality monitoring job.

  • MODEL_BIAS - The schedule is for a bias monitoring job.

  • MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.

VARCHAR