class Aws::SageMaker::Types::CreateAutoMLJobRequest
@note When making an API call, you may pass CreateAutoMLJobRequest
data as a hash: { auto_ml_job_name: "AutoMLJobName", # required input_data_config: [ # required { data_source: { # required s3_data_source: { # required s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix s3_uri: "S3Uri", # required }, }, compression_type: "None", # accepts None, Gzip target_attribute_name: "TargetAttributeName", # required }, ], output_data_config: { # required kms_key_id: "KmsKeyId", s3_output_path: "S3Uri", # required }, problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression auto_ml_job_objective: { metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro, AUC }, auto_ml_job_config: { completion_criteria: { max_candidates: 1, max_runtime_per_training_job_in_seconds: 1, max_auto_ml_job_runtime_in_seconds: 1, }, security_config: { volume_kms_key_id: "KmsKeyId", enable_inter_container_traffic_encryption: false, vpc_config: { security_group_ids: ["SecurityGroupId"], # required subnets: ["SubnetId"], # required }, }, }, role_arn: "RoleArn", # required generate_candidate_definitions_only: false, tags: [ { key: "TagKey", # required value: "TagValue", # required }, ], model_deploy_config: { auto_generate_endpoint_name: false, endpoint_name: "EndpointName", }, }
@!attribute [rw] auto_ml_job_name
Identifies an Autopilot job. The name must be unique to your account and is case-insensitive. @return [String]
@!attribute [rw] input_data_config
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 . Format(s) supported: CSV. Minimum of 500 rows. @return [Array<Types::AutoMLChannel>]
@!attribute [rw] output_data_config
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV. @return [Types::AutoMLOutputDataConfig]
@!attribute [rw] problem_type
Defines the type of supervised learning available for the candidates. Options include: `BinaryClassification`, `MulticlassClassification`, and `Regression`. For more information, see [ Amazon SageMaker Autopilot problem types and algorithm support][1]. [1]: https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html @return [String]
@!attribute [rw] auto_ml_job_objective
Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. @return [Types::AutoMLJobObjective]
@!attribute [rw] auto_ml_job_config
Contains `CompletionCriteria` and `SecurityConfig` settings for the AutoML job. @return [Types::AutoMLJobConfig]
@!attribute [rw] role_arn
The ARN of the role that is used to access the data. @return [String]
@!attribute [rw] generate_candidate_definitions_only
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings. @return [Boolean]
@!attribute [rw] tags
Each tag consists of a key and an optional value. Tag keys must be unique per resource. @return [Array<Types::Tag>]
@!attribute [rw] model_deploy_config
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment. @return [Types::ModelDeployConfig]
@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateAutoMLJobRequest AWS API Documentation
Constants
- SENSITIVE