Struct aws_sdk_sagemaker::operation::create_auto_ml_job::CreateAutoMlJobInput

#[non_exhaustive]
pub struct CreateAutoMlJobInput { /* private fields */ }

Implementations

impl CreateAutoMlJobInput

pub fn auto_ml_job_name(&self) -> Option<&str>

Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

pub fn input_data_config(&self) -> Option<&[AutoMlChannel]>

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.

pub fn output_data_config(&self) -> Option<&AutoMlOutputDataConfig>

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

pub fn problem_type(&self) -> Option<&ProblemType>

Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.

pub fn auto_ml_job_objective(&self) -> Option<&AutoMlJobObjective>

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. For CreateAutoMLJobV2, only Accuracy is supported.

pub fn auto_ml_job_config(&self) -> Option<&AutoMlJobConfig>

A collection of settings used to configure an AutoML job.

pub fn role_arn(&self) -> Option<&str>

The ARN of the role that is used to access the data.

pub fn generate_candidate_definitions_only(&self) -> Option<bool>

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

pub fn tags(&self) -> Option<&[Tag]>

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.

pub fn model_deploy_config(&self) -> Option<&ModelDeployConfig>

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

impl CreateAutoMlJobInput

pub fn builder() -> CreateAutoMlJobInputBuilder

Creates a new builder-style object to manufacture CreateAutoMlJobInput.

impl CreateAutoMlJobInput

pub async fn make_operation( &self, _config: &Config ) -> Result<Operation<CreateAutoMLJob, AwsResponseRetryClassifier>, BuildError>

Consumes the builder and constructs an Operation<CreateAutoMLJob>

Trait Implementations

impl Clone for CreateAutoMlJobInput

fn clone(&self) -> CreateAutoMlJobInput

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateAutoMlJobInput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<CreateAutoMlJobInput> for CreateAutoMlJobInput

fn eq(&self, other: &CreateAutoMlJobInput) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateAutoMlJobInput