auto_ml_jobs

Creates, updates, deletes, gets or lists an auto_ml_jobs resource.

Overview

Name	auto_ml_jobs
Type	Resource
Id	aws.sagemaker.auto_ml_jobs

Fields

The following fields are returned by SELECT queries:

describe_auto_ml_job

Name	Datatype	Description
auto_ml_job_arn	string	Returns the ARN of the AutoML job. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:automl-job/.*</code>)
auto_ml_job_artifacts	object	Returns information on the job's artifacts found in AutoMLJobArtifacts.
auto_ml_job_config	object	Returns the configuration for the AutoML job.
auto_ml_job_name	string	Returns the name of the AutoML job. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,31}</code>)
auto_ml_job_objective	object	Returns the job's objective.
auto_ml_job_secondary_status	string	Returns the secondary status of the AutoML job. (Starting, MaxCandidatesReached, Failed, Stopped, MaxAutoMLJobRuntimeReached, Stopping, CandidateDefinitionsGenerated, Completed, ExplainabilityError, DeployingModel, ModelDeploymentError, GeneratingModelInsightsReport, ModelInsightsError, AnalyzingData, FeatureEngineering, ModelTuning, GeneratingExplainabilityReport, TrainingModels, PreTraining)
auto_ml_job_status	string	Returns the status of the AutoML job. (Completed, InProgress, Failed, Stopped, Stopping)
best_candidate	object	The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.
creation_time	string (date-time)	Returns the creation time of the AutoML job.
end_time	string (date-time)	Returns the end time of the AutoML job.
failure_reason	string	Returns the failure reason for an AutoML job, when applicable.
generate_candidate_definitions_only	boolean	Indicates whether the output for an AutoML job generates candidate definitions only.
input_data_config	array	Returns the input data configuration for the AutoML job.
last_modified_time	string (date-time)	Returns the job's last modified time.
model_deploy_config	object	Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
model_deploy_result	object	Provides information about endpoint for the model deployment.
output_data_config	object	Returns the job's output data config.
partial_failure_reasons	array	Returns a list of reasons for partial failures within an AutoML job.
problem_type	string	Returns the job's problem type. (BinaryClassification, MulticlassClassification, Regression)
resolved_attributes	object	Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.
role_arn	string	The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3. (pattern: <code>arn:aws[a-z-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@-_/]+</code>)

list_auto_ml_jobs

Name	Datatype	Description
auto_ml_job_arn	string	The ARN of the AutoML job. (pattern: <code>arn:aws[a-z-]:sagemaker:[a-z0-9-]:[0-9]{12}:automl-job/.*</code>)
auto_ml_job_name	string	The name of the AutoML job you are requesting. (pattern: <code>[a-zA-Z0-9](-*[a-zA-Z0-9]){0,31}</code>)
auto_ml_job_secondary_status	string	The secondary status of the AutoML job. (Starting, MaxCandidatesReached, Failed, Stopped, MaxAutoMLJobRuntimeReached, Stopping, CandidateDefinitionsGenerated, Completed, ExplainabilityError, DeployingModel, ModelDeploymentError, GeneratingModelInsightsReport, ModelInsightsError, AnalyzingData, FeatureEngineering, ModelTuning, GeneratingExplainabilityReport, TrainingModels, PreTraining)
auto_ml_job_status	string	The status of the AutoML job. (Completed, InProgress, Failed, Stopped, Stopping)
creation_time	string (date-time)	When the AutoML job was created.
end_time	string (date-time)	The end time of an AutoML job.
failure_reason	string	The failure reason of an AutoML job.
last_modified_time	string (date-time)	When the AutoML job was last modified.
partial_failure_reasons	array	The list of reasons for partial failures within an AutoML job.

Methods

The following methods are available for this resource:

Name	Accessible by	Required Params	Optional Params	Description
describe_auto_ml_job	select	region		Returns information about an AutoML job created by calling CreateAutoMLJob. AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.
list_auto_ml_jobs	select	region		Request a list of jobs.
create_auto_ml_job	insert	region, AutoMLJobName, InputDataConfig, OutputDataConfig, RoleArn		Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https:​//docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide. We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2. You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
stop_auto_ml_job	exec	region, AutoMLJobName		A method for forcing a running job to shut down.

Parameters

Parameters can be passed in the WHERE clause of a query. Check the Methods section to see which parameters are required or optional for each operation.

Name	Datatype	Description
region	string	AWS region (default: us-east-1)

SELECT examples

describe_auto_ml_job

Returns information about an AutoML job created by calling CreateAutoMLJob. AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob.

SELECT
auto_ml_job_arn,
auto_ml_job_artifacts,
auto_ml_job_config,
auto_ml_job_name,
auto_ml_job_objective,
auto_ml_job_secondary_status,
auto_ml_job_status,
best_candidate,
creation_time,
end_time,
failure_reason,
generate_candidate_definitions_only,
input_data_config,
last_modified_time,
model_deploy_config,
model_deploy_result,
output_data_config,
partial_failure_reasons,
problem_type,
resolved_attributes,
role_arn
FROM aws.sagemaker.auto_ml_jobs
WHERE region = '{{ region }}' -- required
;

list_auto_ml_jobs

Request a list of jobs.

SELECT
auto_ml_job_arn,
auto_ml_job_name,
auto_ml_job_secondary_status,
auto_ml_job_status,
creation_time,
end_time,
failure_reason,
last_modified_time,
partial_failure_reasons
FROM aws.sagemaker.auto_ml_jobs
WHERE region = '{{ region }}' -- required
;

INSERT examples

create_auto_ml_job

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide. We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility. CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2. You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.

INSERT INTO aws.sagemaker.auto_ml_jobs (
AutoMLJobName,
InputDataConfig,
OutputDataConfig,
ProblemType,
AutoMLJobObjective,
AutoMLJobConfig,
RoleArn,
GenerateCandidateDefinitionsOnly,
Tags,
ModelDeployConfig,
region
)
SELECT 
'{{ AutoMLJobName }}' /* required */,
'{{ InputDataConfig }}' /* required */,
'{{ OutputDataConfig }}' /* required */,
'{{ ProblemType }}',
'{{ AutoMLJobObjective }}',
'{{ AutoMLJobConfig }}',
'{{ RoleArn }}' /* required */,
{{ GenerateCandidateDefinitionsOnly }},
'{{ Tags }}',
'{{ ModelDeployConfig }}',
'{{ region }}'
RETURNING
auto_ml_job_arn
;

Manifest

# Description fields are for documentation purposes
- name: auto_ml_jobs
props:
  - name: region
    value: "{{ region }}"
    description: Required parameter for the auto_ml_jobs resource.
  - name: AutoMLJobName
    value: "{{ AutoMLJobName }}"
    description: |
      Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
  - name: InputDataConfig
    description: |
      An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
    value:
      - DataSource:
          S3DataSource:
            S3DataType: "{{ S3DataType }}"
            S3Uri: "{{ S3Uri }}"
        CompressionType: "{{ CompressionType }}"
        TargetAttributeName: "{{ TargetAttributeName }}"
        ContentType: "{{ ContentType }}"
        ChannelType: "{{ ChannelType }}"
        SampleWeightAttributeName: "{{ SampleWeightAttributeName }}"
  - name: OutputDataConfig
    description: |
      Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
    value:
      KmsKeyId: "{{ KmsKeyId }}"
      S3OutputPath: "{{ S3OutputPath }}"
  - name: ProblemType
    value: "{{ ProblemType }}"
    description: |
      Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.
    valid_values: ['BinaryClassification', 'MulticlassClassification', 'Regression']
  - name: AutoMLJobObjective
    description: |
      Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
    value:
      MetricName: "{{ MetricName }}"
  - name: AutoMLJobConfig
    description: |
      A collection of settings used to configure an AutoML job.
    value:
      CompletionCriteria:
        MaxCandidates: {{ MaxCandidates }}
        MaxRuntimePerTrainingJobInSeconds: {{ MaxRuntimePerTrainingJobInSeconds }}
        MaxAutoMLJobRuntimeInSeconds: {{ MaxAutoMLJobRuntimeInSeconds }}
      SecurityConfig:
        VolumeKmsKeyId: "{{ VolumeKmsKeyId }}"
        EnableInterContainerTrafficEncryption: {{ EnableInterContainerTrafficEncryption }}
        VpcConfig:
          SecurityGroupIds:
            - "{{ SecurityGroupIds }}"
          Subnets:
            - "{{ Subnets }}"
      CandidateGenerationConfig:
        FeatureSpecificationS3Uri: "{{ FeatureSpecificationS3Uri }}"
        AlgorithmsConfig:
          - AutoMLAlgorithms: "{{ AutoMLAlgorithms }}"
      DataSplitConfig:
        ValidationFraction: {{ ValidationFraction }}
      Mode: "{{ Mode }}"
  - name: RoleArn
    value: "{{ RoleArn }}"
    description: |
      The ARN of the role that is used to access the data.
  - name: GenerateCandidateDefinitionsOnly
    value: {{ GenerateCandidateDefinitionsOnly }}
    description: |
      Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
  - name: Tags
    description: |
      An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
    value:
      - Key: "{{ Key }}"
        Value: "{{ Value }}"
  - name: ModelDeployConfig
    description: |
      Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
    value:
      AutoGenerateEndpointName: {{ AutoGenerateEndpointName }}
      EndpointName: "{{ EndpointName }}"

Lifecycle Methods

stop_auto_ml_job

A method for forcing a running job to shut down.

EXEC aws.sagemaker.auto_ml_jobs.stop_auto_ml_job 
@region='{{ region }}' --required 
@@json=
'{
"AutoMLJobName": "{{ AutoMLJobName }}"
}'
;