Struct aws_sdk_sagemaker::input::create_auto_ml_job_input::Builder

pub struct Builder { /* private fields */ }

A builder for CreateAutoMlJobInput.

Implementations

impl Builder

pub fn auto_ml_job_name(self, input: impl Into<String>) -> Self

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

pub fn set_auto_ml_job_name(self, input: Option<String>) -> Self

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

pub fn input_data_config(self, input: AutoMlChannel) -> Self

Appends an item to input_data_config.

To override the contents of this collection use set_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, 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 set_input_data_config(self, input: Option<Vec<AutoMlChannel>>) -> Self

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, 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, input: AutoMlOutputDataConfig) -> Self

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

pub fn set_output_data_config(
    self,
    input: Option<AutoMlOutputDataConfig>
) -> Self

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, input: ProblemType) -> Self

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

pub fn set_problem_type(self, input: Option<ProblemType>) -> Self

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

pub fn auto_ml_job_objective(self, input: AutoMlJobObjective) -> Self

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.

pub fn set_auto_ml_job_objective(self, input: Option<AutoMlJobObjective>) -> Self

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.

pub fn auto_ml_job_config(self, input: AutoMlJobConfig) -> Self

A collection of settings used to configure an AutoML job.

pub fn set_auto_ml_job_config(self, input: Option<AutoMlJobConfig>) -> Self

A collection of settings used to configure an AutoML job.

pub fn role_arn(self, input: impl Into<String>) -> Self

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

pub fn set_role_arn(self, input: Option<String>) -> Self

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

pub fn generate_candidate_definitions_only(self, input: bool) -> Self

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

pub fn set_generate_candidate_definitions_only(self, input: Option<bool>) -> Self

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

pub fn tags(self, input: Tag) -> Self

Appends an item to tags.

To override the contents of this collection use set_tags.

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

pub fn model_deploy_config(self, input: ModelDeployConfig) -> Self

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

pub fn set_model_deploy_config(self, input: Option<ModelDeployConfig>) -> Self

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

pub fn build(self) -> Result<CreateAutoMlJobInput, BuildError>

Consumes the builder and constructs a CreateAutoMlJobInput.

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

fn eq(&self, other: &Builder) -> 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 Builder