Struct aws_sdk_sagemaker::input::CreateAutoMlJobInput

#[non_exhaustive]
pub struct CreateAutoMlJobInput {
    pub auto_ml_job_name: Option<String>,
    pub input_data_config: Option<Vec<AutoMlChannel>>,
    pub output_data_config: Option<AutoMlOutputDataConfig>,
    pub problem_type: Option<ProblemType>,
    pub auto_ml_job_objective: Option<AutoMlJobObjective>,
    pub auto_ml_job_config: Option<AutoMlJobConfig>,
    pub role_arn: Option<String>,
    pub generate_candidate_definitions_only: bool,
    pub tags: Option<Vec<Tag>>,
    pub model_deploy_config: Option<ModelDeployConfig>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

auto_ml_job_name: Option<String>

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

input_data_config: Option<Vec<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 . Format(s) supported: CSV. Minimum of 500 rows.

output_data_config: Option<AutoMlOutputDataConfig>

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

problem_type: Option<ProblemType>

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.

auto_ml_job_objective: 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.

auto_ml_job_config: Option<AutoMlJobConfig>

Contains CompletionCriteria and SecurityConfig settings for the AutoML job.

role_arn: Option<String>

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

generate_candidate_definitions_only: bool

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

tags: Option<Vec<Tag>>

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

model_deploy_config: Option<ModelDeployConfig>

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

Implementations

impl CreateAutoMlJobInput

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

Consumes the builder and constructs an Operation<CreateAutoMLJob>

pub fn builder() -> Builder

Creates a new builder-style object to manufacture CreateAutoMlJobInput

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 . Format(s) supported: CSV. Minimum of 500 rows.

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 available for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

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.

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

Contains CompletionCriteria and SecurityConfig settings for the 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) -> 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]>

Each tag consists of a key and an optional value. 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.

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: &CreateAutoMlJobInput) -> bool

This method tests for !=.

impl StructuralPartialEq for CreateAutoMlJobInput