Struct CreateAutoMLJobRequest

pub struct CreateAutoMLJobRequest {
    pub auto_ml_job_config: Option<AutoMLJobConfig>,
    pub auto_ml_job_name: String,
    pub auto_ml_job_objective: Option<AutoMLJobObjective>,
    pub generate_candidate_definitions_only: Option<bool>,
    pub input_data_config: Vec<AutoMLChannel>,
    pub model_deploy_config: Option<ModelDeployConfig>,
    pub output_data_config: AutoMLOutputDataConfig,
    pub problem_type: Option<String>,
    pub role_arn: String,
    pub tags: Option<Vec<Tag>>,
}

Fields

auto_ml_job_config: Option<AutoMLJobConfig>

Contains CompletionCriteria and SecurityConfig settings for the AutoML job.

auto_ml_job_name: String

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

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.

generate_candidate_definitions_only: Option<bool>

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

input_data_config: 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.

model_deploy_config: Option<ModelDeployConfig>

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

output_data_config: 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<String>

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.

role_arn: String

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

tags: Option<Vec<Tag>>

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

Trait Implementations

impl Clone for CreateAutoMLJobRequest

fn clone(&self) -> CreateAutoMLJobRequest

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateAutoMLJobRequest

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

Formats the value using the given formatter.

impl Default for CreateAutoMLJobRequest

fn default() -> CreateAutoMLJobRequest

Returns the “default value” for a type.

impl PartialEq for CreateAutoMLJobRequest

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl Serialize for CreateAutoMLJobRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CreateAutoMLJobRequest